Import dataset into R from your directory Here is my directory

SEED GERMINATION PORTION OF THE EXPERIEMENT BEGINS

Time to first germination in a given pot

Full Statistical model

Diagnostic Graphs

day_t0_germ_model= lmer((t0_germ)^-1~precip*soil_root+(1|block), data= dataSG_seed_1st_germ_trt)
qqPlot(resid(day_t0_germ_model))

##  3 85 
##  2 56
hist(resid(day_t0_germ_model))

shapiro.test(resid(day_t0_germ_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(day_t0_germ_model)
## W = 0.96991, p-value = 0.1514

Raw Statistical output

anova(day_t0_germ_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                     Sum Sq   Mean Sq NumDF  DenDF F value  Pr(>F)  
## precip           0.0119463 0.0119463     1 50.215  3.5356 0.06587 .
## soil_root        0.0313006 0.0156503     2 50.655  4.6318 0.01421 *
## precip:soil_root 0.0045726 0.0022863     2 51.349  0.6766 0.51279  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(day_t0_germ_model, pairwise~soil_root)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B        0.195 0.0148 11.9    0.162    0.227
##  S.B        0.140 0.0176 21.6    0.103    0.176
##  L.R        0.195 0.0150 12.7    0.163    0.228
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast   estimate     SE   df t.ratio p.value
##  L.B - S.B  0.054728 0.0201 49.2  2.726  0.0236 
##  L.B - L.R -0.000469 0.0183 51.3 -0.026  0.9996 
##  S.B - L.R -0.055197 0.0204 50.1 -2.702  0.0250 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(day_t0_germ_model, pairwise~soil_root|precip)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B        0.187 0.0186 24.8   0.1488    0.226
##  S.B        0.111 0.0223 35.9   0.0656    0.156
##  L.R        0.187 0.0168 19.7   0.1519    0.222
## 
## precip = D:
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B        0.202 0.0202 29.5   0.1608    0.243
##  S.B        0.169 0.0254 42.2   0.1178    0.220
##  L.R        0.203 0.0225 34.9   0.1576    0.249
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast   estimate     SE   df t.ratio p.value
##  L.B - S.B  0.076424 0.0272 51.2  2.807  0.0190 
##  L.B - L.R  0.000133 0.0227 49.8  0.006  1.0000 
##  S.B - L.R -0.076292 0.0257 50.0 -2.964  0.0127 
## 
## precip = D:
##  contrast   estimate     SE   df t.ratio p.value
##  L.B - S.B  0.033031 0.0303 49.8  1.091  0.5241 
##  L.B - L.R -0.001071 0.0283 51.2 -0.038  0.9992 
##  S.B - L.R -0.034101 0.0325 51.9 -1.048  0.5504 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Days to germination
Df Resid.Df F.value P.value
precip 1 50.22 3.54 0.066
soil_root 2 50.66 4.63 0.014
precip:soil_root 2 51.35 0.68 0.513

Graph for publication

## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.

Presence Days to first Germination

Diagnostic Graphs

dataSG_seed_1st_germ_pres=subset(dataSG_seed_1st_germ_trt, root_association =="B")
unique(dataSG_seed_1st_germ_pres$soil_root)
## [1] L.B S.B
## Levels: L.B S.B L.R S.R
nrow(dataSG_seed_1st_germ_pres)
## [1] 60
day_t0_germ_model_pres= lmer((t0_germ)^-1~precip*soil_root+(1|block), data= dataSG_seed_1st_germ_pres)
qqPlot(resid(day_t0_germ_model_pres))

## 85 24 
## 33  6
hist(resid(day_t0_germ_model_pres))

shapiro.test(resid(day_t0_germ_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(day_t0_germ_model_pres)
## W = 0.98241, p-value = 0.8234

Raw Statistical output

anova(day_t0_germ_model_pres)
## Type III Analysis of Variance Table with Satterthwaite's method
##                     Sum Sq   Mean Sq NumDF  DenDF F value   Pr(>F)   
## precip           0.0122308 0.0122308     1 29.562  4.2813 0.047376 * 
## soil_root        0.0252094 0.0252094     1 28.581  8.8243 0.005968 **
## precip:soil_root 0.0052936 0.0052936     1 30.328  1.8530 0.183466   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(day_t0_germ_model_pres, pairwise~soil_root)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B        0.196 0.0168 5.72   0.1546    0.238
##  S.B        0.141 0.0188 9.08   0.0987    0.184
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate     SE   df t.ratio p.value
##  L.B - S.B   0.0548 0.0185 28.2 2.966   0.0061 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger
emmeans(day_t0_germ_model_pres, pairwise~soil_root|precip)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B        0.190 0.0197 10.4   0.1459    0.233
##  S.B        0.109 0.0227 15.8   0.0607    0.157
## 
## precip = D:
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B        0.203 0.0211 12.6   0.1569    0.248
##  S.B        0.174 0.0256 19.9   0.1201    0.227
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate     SE   df t.ratio p.value
##  L.B - S.B   0.0807 0.0255 29.9 3.161   0.0036 
## 
## precip = D:
##  contrast  estimate     SE   df t.ratio p.value
##  L.B - S.B   0.0290 0.0280 28.7 1.033   0.3103 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Days to germination
Df Resid.Df F.value P.value
precip 1 29.56 4.28 0.047
soil_root 1 28.58 8.82 0.006
precip:soil_root 1 30.33 1.85 0.183

Origin Days to first Germination

Diagnostic Graphs

dataSG_seed_1st_germ_orig=subset(dataSG_seed_1st_germ_trt, soil_status =="L")
unique(dataSG_seed_1st_germ_orig$soil_root)
## [1] L.R L.B
## Levels: L.B S.B L.R S.R
nrow(dataSG_seed_1st_germ_orig)
## [1] 60
#60


day_t0_germ_model_orig= lmer((t0_germ)^-2~precip*soil_root+(1|block), data= dataSG_seed_1st_germ_orig)
qqPlot(resid(day_t0_germ_model_orig))

##  3 66 
##  2 37
hist(resid(day_t0_germ_model_orig))

shapiro.test(resid(day_t0_germ_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(day_t0_germ_model_orig)
## W = 0.93385, p-value = 0.01286
#0.01286

Raw Statistical output

anova(day_t0_germ_model_orig)
## Type III Analysis of Variance Table with Satterthwaite's method
##                      Sum Sq    Mean Sq NumDF  DenDF F value Pr(>F)
## precip           0.00040305 0.00040305     1 39.847  1.0708 0.3070
## soil_root        0.00001075 0.00001075     1 40.258  0.0285 0.8667
## precip:soil_root 0.00000263 0.00000263     1 38.791  0.0070 0.9339
#emmeans(day_t0_germ_model_orig, pairwise~soil_root)
#emmeans(day_t0_germ_model_orig, pairwise~soil_root|precip)

Formatted Anova table

Days to germination
Df Resid.Df F.value P.value
precip 1 39.85 1.07 0.307
soil_root 1 40.26 0.03 0.867
precip:soil_root 1 38.79 0.01 0.934

Final number of germinates

Diagnostic Graphs

#Total germination
#convert to percentages
fin_dataSG_seed_surv_trt_tot_seedling$prop_tot_num_germ=fin_dataSG_seed_surv_trt_tot_seedling$tot_num_germ/10
seed_germ_model= glmer.nb((tot_num_germ)~precip*soil_root+(1|block), data= fin_dataSG_seed_surv_trt_tot_seedling)
## Warning in theta.ml(Y, mu, weights = object@resp$weights, limit = limit, :
## iteration limit reached
## boundary (singular) fit: see ?isSingular
qqPlot(resid(seed_germ_model))

## 37 42 
## 59 61
hist(resid(seed_germ_model))

shapiro.test(resid(seed_germ_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_germ_model)
## W = 0.93147, p-value = 0.0001422
#p-value = 0.0001422
plot(seed_germ_model)

#Chad's code for checking for over dispersion
overdisp_fun <- function(model) {
  vpars <- function(m) {
    nrow(m)*(nrow(m)+1)/2
  }
  model.df <- sum(sapply(VarCorr(model),vpars))+length(fixef(model))
  rdf <- nrow(model.frame(model))-model.df
  rp <- residuals(model,type="pearson")
  Pearson.chisq <- sum(rp^2)
  prat <- Pearson.chisq/rdf
  pval <- pchisq(Pearson.chisq, df=rdf, lower.tail=FALSE)
  c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval)
}

overdisp_fun(seed_germ_model)
##      chisq      ratio        rdf          p 
## 61.5017682  0.7409852 83.0000000  0.9629765

Raw Statistical output

summary(seed_germ_model)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: Negative Binomial(54342.88)  ( log )
## Formula: (tot_num_germ) ~ precip * soil_root + (1 | block)
##    Data: fin_dataSG_seed_surv_trt_tot_seedling
## 
##      AIC      BIC   logLik deviance df.resid 
##    222.9    242.9   -103.5    206.9       82 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.12545 -0.73029 -0.08396  0.40823  2.00830 
## 
## Random effects:
##  Groups Name        Variance  Std.Dev. 
##  block  (Intercept) 5.981e-11 7.733e-06
## Number of obs: 90, groups:  block, 5
## 
## Fixed effects:
##                    Estimate Std. Error z value Pr(>|z|)  
## (Intercept)        -0.12688    0.11619  -1.092   0.2748  
## precip1             0.24080    0.11618   2.073   0.0382 *
## soil_root1          0.21058    0.15408   1.367   0.1717  
## soil_root2         -0.39015    0.17898  -2.180   0.0293 *
## precip1:soil_root1 -0.08811    0.15408  -0.572   0.5674  
## precip1:soil_root2 -0.12923    0.17898  -0.722   0.4703  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) precp1 sl_rt1 sl_rt2 pr1:_1
## precip1     -0.211                            
## soil_root1  -0.178  0.072                     
## soil_root2   0.245  0.039 -0.556              
## prcp1:sl_r1  0.072 -0.178 -0.185  0.027       
## prcp1:sl_r2  0.039  0.245  0.027 -0.153 -0.556
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Anova(seed_germ_model,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: (tot_num_germ)
##                   Chisq Df Pr(>Chisq)  
## (Intercept)      1.1925  1    0.27483  
## precip           4.2959  1    0.03821 *
## soil_root        4.7863  2    0.09134 .
## precip:soil_root 1.8911  2    0.38847  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(seed_germ_model,pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B        0.236 0.229 Inf    -0.212    0.6849
##  S.B       -0.405 0.315 Inf    -1.023    0.2119
##  L.R        0.511 0.200 Inf     0.120    0.9021
## 
## precip = D:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B       -0.069 0.267 Inf    -0.592    0.4535
##  S.B       -0.629 0.352 Inf    -1.318    0.0609
##  L.R       -0.405 0.315 Inf    -1.023    0.2122
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B    0.642 0.389 Inf  1.650  0.2248 
##  L.B - L.R   -0.274 0.304 Inf -0.903  0.6381 
##  S.B - L.R   -0.916 0.373 Inf -2.457  0.0373 
## 
## precip = D:
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B    0.560 0.441 Inf  1.269  0.4126 
##  L.B - L.R    0.336 0.413 Inf  0.815  0.6938 
##  S.B - L.R   -0.223 0.472 Inf -0.472  0.8843 
## 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Number of seeds that germinated
Df Chi.Sq P.value
(Intercept) 1 1.19 0.275
precip 1 4.30 0.038
soil_root 2 4.79 0.091
precip:soil_root 2 1.89 0.388

Presence Number of Seeds that Geminated

Diagnostic Graphs

fin_dataSG_seed_surv_trt_tot_seedling_pres=subset(fin_dataSG_seed_surv_trt_tot_seedling, root_association =="B")
seed_germ_model_pres= glmer.nb((tot_num_germ)~precip*soil_root+(1|block), data= fin_dataSG_seed_surv_trt_tot_seedling_pres)
## Warning in theta.ml(Y, mu, weights = object@resp$weights, limit = limit, :
## iteration limit reached
qqPlot(resid(seed_germ_model_pres))

## 42 37 
## 43 41
hist(resid(seed_germ_model_pres))

shapiro.test(resid(seed_germ_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_germ_model_pres)
## W = 0.92034, p-value = 0.0007916
#p-value = 0.0007916
plot(seed_germ_model_pres)

Raw Statistical output

summary(seed_germ_model_pres)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: Negative Binomial(52724.35)  ( log )
## Formula: (tot_num_germ) ~ precip * soil_root + (1 | block)
##    Data: fin_dataSG_seed_surv_trt_tot_seedling_pres
## 
##      AIC      BIC   logLik deviance df.resid 
##    146.5    159.1    -67.3    134.5       54 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.14275 -0.76619 -0.07703  0.58158  1.95560 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0.03097  0.176   
## Number of obs: 60, groups:  block, 5
## 
## Fixed effects:
##                    Estimate Std. Error z value Pr(>|z|)  
## (Intercept)        -0.23211    0.17146  -1.354   0.1758  
## precip1             0.13213    0.14743   0.896   0.3701  
## soil_root1          0.30036    0.14750   2.036   0.0417 *
## precip1:soil_root1  0.02056    0.14753   0.139   0.8892  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) precp1 sl_rt1
## precip1     -0.106              
## soil_root1  -0.246  0.018       
## prcp1:sl_r1  0.015 -0.289 -0.126
Anova(seed_germ_model_pres,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: (tot_num_germ)
##                   Chisq Df Pr(>Chisq)  
## (Intercept)      1.8325  1    0.17583  
## precip           0.8033  1    0.37012  
## soil_root        4.1466  1    0.04172 *
## precip:soil_root 0.0194  1    0.88918  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(seed_germ_model_pres,pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root  emmean    SE  df asymp.LCL asymp.UCL
##  L.B        0.2209 0.246 Inf    -0.261    0.7024
##  S.B       -0.4209 0.327 Inf    -1.063    0.2210
## 
## precip = D:
##  soil_root  emmean    SE  df asymp.LCL asymp.UCL
##  L.B       -0.0844 0.281 Inf    -0.635    0.4664
##  S.B       -0.6440 0.363 Inf    -1.356    0.0681
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B    0.642 0.390 Inf 1.645   0.0999 
## 
## precip = D:
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B    0.560 0.443 Inf 1.264   0.2062 
## 
## Results are given on the log (not the response) scale.

Formatted Anova table

Number of seeds that germinated
Df Chi.Sq P.value
(Intercept) 1 1.83 0.176
precip 1 0.80 0.370
soil_root 1 4.15 0.042
precip:soil_root 1 0.02 0.889

Origin Number of Seeds that Geminated

Diagnostic Graphs

fin_dataSG_seed_surv_trt_tot_seedling_org=subset(fin_dataSG_seed_surv_trt_tot_seedling, soil_status =="L")

seed_germ_model_org= glmer.nb((tot_num_germ)~precip*soil_root+(1|block), data= fin_dataSG_seed_surv_trt_tot_seedling_org)
## Warning in theta.ml(Y, mu, weights = object@resp$weights, limit = limit, :
## iteration limit reached
## boundary (singular) fit: see ?isSingular
qqPlot(resid(seed_germ_model_org))

## 58  6 
## 49 26
hist(resid(seed_germ_model_org))

shapiro.test(resid(seed_germ_model_org))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_germ_model_org)
## W = 0.95425, p-value = 0.02477
#p-value = 0.02477
plot(seed_germ_model_org)

Raw Statistical output

summary(seed_germ_model_org)
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: Negative Binomial(77187.13)  ( log )
## Formula: (tot_num_germ) ~ precip * soil_root + (1 | block)
##    Data: fin_dataSG_seed_surv_trt_tot_seedling_org
## 
##      AIC      BIC   logLik deviance df.resid 
##    159.2    171.8    -73.6    147.2       54 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -1.12545 -0.59142  0.06902  0.40825  1.80738 
## 
## Random effects:
##  Groups Name        Variance Std.Dev.
##  block  (Intercept) 0        0       
## Number of obs: 60, groups:  block, 5
## 
## Fixed effects:
##                    Estimate Std. Error z value Pr(>|z|)  
## (Intercept)         0.06818    0.12820   0.532   0.5948  
## precip1             0.30542    0.12820   2.382   0.0172 *
## soil_root1          0.01551    0.12820   0.121   0.9037  
## precip1:soil_root1 -0.15272    0.12820  -1.191   0.2335  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) precp1 sl_rt1
## precip1     -0.297              
## soil_root1  -0.064  0.158       
## prcp1:sl_r1  0.158 -0.064 -0.297
## convergence code: 0
## boundary (singular) fit: see ?isSingular
Anova(seed_germ_model_org,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: (tot_num_germ)
##                   Chisq Df Pr(>Chisq)  
## (Intercept)      0.2829  1     0.5948  
## precip           5.6755  1     0.0172 *
## soil_root        0.0146  1     0.9037  
## precip:soil_root 1.4191  1     0.2335  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(seed_germ_model_org,pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B        0.236 0.229 Inf    -0.213     0.685
##  L.R        0.511 0.200 Inf     0.119     0.903
## 
## precip = D:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B       -0.069 0.266 Inf    -0.590     0.452
##  L.R       -0.405 0.316 Inf    -1.025     0.214
## 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - L.R   -0.274 0.304 Inf -0.902  0.3668 
## 
## precip = D:
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - L.R    0.336 0.413 Inf  0.815  0.4152 
## 
## Results are given on the log (not the response) scale.

Formatted Anova table

Number of seeds that germinated
Df Chi.Sq P.value
(Intercept) 1 0.28 0.59
precip 1 5.68 0.02
soil_root 1 0.01 0.90
precip:soil_root 1 1.42 0.23

Graph for publication

## Warning: package 'gdtools' was built under R version 4.0.2

Survival given germination

Full Statistical model

Diagnostic Graphs

seed_surv_germ_model= glmer(surv_germ~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_trt_w_germ, family="binomial")
qqPlot(resid(seed_surv_germ_model))

## 24 71 
## 14 47
hist(resid(seed_surv_germ_model))

shapiro.test(resid(seed_surv_germ_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_surv_germ_model)
## W = 0.79433, p-value = 1.174e-07
#1.174e-07
plot(seed_surv_germ_model)

Raw Statistical output

Anova(seed_surv_germ_model, type=3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv_germ
##                   Chisq Df Pr(>Chisq)  
## (Intercept)      4.6354  1    0.03132 *
## precip           0.0623  1    0.80290  
## soil_root        7.0596  2    0.02931 *
## precip:soil_root 2.3638  2    0.30669  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#soil_root          9.8880  2   0.007126 **
#soil_root        7.0596  2    0.02931 *
#precip:soil_root   4.3070  2   0.116077 
emmeans(seed_surv_germ_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B        1.946 0.813 Inf     0.353      3.54
##  S.B       -0.243 0.746 Inf    -1.705      1.22
##  L.R        2.015 0.850 Inf     0.350      3.68
## 
## Results are averaged over the levels of: precip 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B   2.1897 0.942 Inf  2.324  0.0526 
##  L.B - L.R  -0.0682 0.977 Inf -0.070  0.9973 
##  S.B - L.R  -2.2579 0.996 Inf -2.266  0.0606 
## 
## Results are averaged over the levels of: precip 
## Results are given on the log odds ratio (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(seed_surv_germ_model, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B        1.644 0.928 Inf   -0.1741     3.462
##  S.B        0.731 0.929 Inf   -1.0904     2.552
##  L.R        1.629 0.846 Inf   -0.0287     3.287
## 
## precip = D:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B        2.249 1.187 Inf   -0.0779     4.576
##  S.B       -1.217 1.087 Inf   -3.3480     0.913
##  L.R        2.400 1.270 Inf   -0.0886     4.888
## 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate   SE  df z.ratio p.value
##  L.B - S.B   0.9133 1.16 Inf  0.787  0.7110 
##  L.B - L.R   0.0146 1.08 Inf  0.013  0.9999 
##  S.B - L.R  -0.8987 1.06 Inf -0.845  0.6748 
## 
## precip = D:
##  contrast  estimate   SE  df z.ratio p.value
##  L.B - S.B   3.4662 1.53 Inf  2.259  0.0617 
##  L.B - L.R  -0.1510 1.59 Inf -0.095  0.9951 
##  S.B - L.R  -3.6172 1.67 Inf -2.168  0.0767 
## 
## Results are given on the log odds ratio (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Survival given germination
Df Chi.Sq P.value
(Intercept) 1 4.64 0.031
precip 1 0.06 0.803
soil_root 2 7.06 0.029
precip:soil_root 2 2.36 0.307
Graph for publicati on
<img src="MERDS_R_c ode_SG _reAnalysi s_20200828_files/figure-html/unnamed-chunk-29-1.png" width=“672” />

Presence survival given germination

Diagnostic Graphs

fin_dataSG_biomass_seed_surv_pres_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_w_germ, root_association =="B")
seed_surv_germ_model_pres= glmer(surv_germ~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_pres_w_germ, family="binomial")
## boundary (singular) fit: see ?isSingular
qqPlot(resid(seed_surv_germ_model_pres))

## 24 71 
##  6 24
hist(resid(seed_surv_germ_model_pres))

shapiro.test(resid(seed_surv_germ_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_surv_germ_model_pres)
## W = 0.81516, p-value = 3.305e-05
#3.305e-05
plot(seed_surv_germ_model_pres)

Raw Statistical output

Anova(seed_surv_germ_model_pres, type=3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv_germ
##                   Chisq Df Pr(>Chisq)  
## (Intercept)      4.3867  1    0.03622 *
## precip           0.1268  1    0.72178  
## soil_root        5.3138  1    0.02116 *
## precip:soil_root 1.0721  1    0.30047  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(seed_surv_germ_model_pres, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root  emmean    SE  df asymp.LCL asymp.UCL
##  L.B        1.9033 0.654 Inf     0.621      3.19
##  S.B       -0.0912 0.566 Inf    -1.201      1.02
## 
## Results are averaged over the levels of: precip 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B     1.99 0.865 Inf 2.305   0.0212 
## 
## Results are averaged over the levels of: precip 
## Results are given on the log odds ratio (not the response) scale.
emmeans(seed_surv_germ_model_pres, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B        1.609 0.775 Inf    0.0913      3.13
##  S.B        0.511 0.730 Inf   -0.9205      1.94
## 
## precip = D:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B        2.197 1.054 Inf    0.1312      4.26
##  S.B       -0.693 0.866 Inf   -2.3905      1.00
## 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate   SE  df z.ratio p.value
##  L.B - S.B     1.10 1.06 Inf 1.032   0.3021 
## 
## precip = D:
##  contrast  estimate   SE  df z.ratio p.value
##  L.B - S.B     2.89 1.36 Inf 2.119   0.0341 
## 
## Results are given on the log odds ratio (not the response) scale.

Formatted Anova table

Survival given germination
Df Chi.Sq P.value
(Intercept) 1 4.39 0.036
precip 1 0.13 0.722
soil_root 1 5.31 0.021
precip:soil_root 1 1.07 0.300

Origin survival given germination

Diagnostic Graphs

fin_dataSG_biomass_seed_surv_orig_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_w_germ, soil_status =="L")
seed_surv_germ_model_orig= glmer(surv_germ~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_orig_w_germ, family="binomial")
qqPlot(resid(seed_surv_germ_model_orig))

## 24 71 
## 14 41
hist(resid(seed_surv_germ_model_orig))

shapiro.test(resid(seed_surv_germ_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_surv_germ_model_orig)
## W = 0.54238, p-value = 1.23e-10
#1.23e-10
plot(seed_surv_germ_model_orig)

Raw Statistical output

Anova(seed_surv_germ_model_orig, type=3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv_germ
##                    Chisq Df Pr(>Chisq)    
## (Intercept)      11.8503  1  0.0005765 ***
## precip            0.3965  1  0.5288941    
## soil_root         0.0390  1  0.8433818    
## precip:soil_root  0.0001  1  0.9942924    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(seed_surv_germ_model_orig, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B         1.92 0.700 Inf     0.551      3.30
##  L.R         1.74 0.714 Inf     0.341      3.14
## 
## Results are averaged over the levels of: precip 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - L.R    0.184 0.931 Inf 0.198   0.8434 
## 
## Results are averaged over the levels of: precip 
## Results are given on the log odds ratio (not the response) scale.
emmeans(seed_surv_germ_model_orig, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B         1.63 0.819 Inf    0.0263      3.24
##  L.R         1.45 0.724 Inf    0.0350      2.87
## 
## precip = D:
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B         2.22 1.086 Inf    0.0865      4.34
##  L.R         2.03 1.139 Inf   -0.2070      4.26
## 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate   SE  df z.ratio p.value
##  L.B - L.R    0.177 1.03 Inf 0.172   0.8634 
## 
## precip = D:
##  contrast  estimate   SE  df z.ratio p.value
##  L.B - L.R    0.190 1.53 Inf 0.125   0.9009 
## 
## Results are given on the log odds ratio (not the response) scale.

Formatted Anova table

Survival given germination
Df Chi.Sq P.value
(Intercept) 1 11.85 0.001
precip 1 0.40 0.529
soil_root 1 0.04 0.843
precip:soil_root 1 0.00 0.994

Biomass given germination

Diagnostic Graphs

seed_biomas_model= lmer(log(total_biomass)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_trt_given_germ)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(seed_biomas_model))

## 12 52 
##  6 23
hist(resid(seed_biomas_model))

shapiro.test(resid(seed_biomas_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_biomas_model)
## W = 0.9757, p-value = 0.457
#p-value = 0.457

Raw Statistical output

anova(seed_biomas_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## precip            0.7614  0.7614     1    39  0.8395 0.3651603    
## soil_root         7.2196  3.6098     2    39  3.9802 0.0267300 *  
## precip:soil_root 18.1525  9.0763     2    39 10.0075 0.0003104 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(seed_biomas_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        -3.03 0.222 14.6    -3.51   -2.555
##  S.B        -1.76 0.427 28.4    -2.63   -0.885
##  L.R        -2.85 0.237 14.2    -3.36   -2.342
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -1.271 0.475 38.9 -2.677  0.0285 
##  L.B - L.R   -0.181 0.322 37.5 -0.563  0.8404 
##  S.B - L.R    1.089 0.495 38.0  2.201  0.0839 
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(seed_biomas_model, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B       -2.639 0.309 28.4    -3.27    -2.01
##  S.B       -3.184 0.435 37.2    -4.07    -2.30
##  L.R       -2.281 0.278 25.8    -2.85    -1.71
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B       -3.421 0.323 31.2    -4.08    -2.76
##  S.B       -0.335 0.740 32.3    -1.84     1.17
##  L.R       -3.417 0.379 30.9    -4.19    -2.64
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B  0.54492 0.538 38.0  1.014  0.5729 
##  L.B - L.R -0.35848 0.413 36.5 -0.868  0.6635 
##  S.B - L.R -0.90339 0.511 36.3 -1.767  0.1951 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B -3.08610 0.807 37.8 -3.822  0.0014 
##  L.B - L.R -0.00398 0.488 37.1 -0.008  1.0000 
##  S.B - L.R  3.08212 0.863 32.0  3.572  0.0032 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Total biomass given germination
Df Resid.Df F.value P.value
1 39 0.84 0.365
2 39 3.98 0.027
2 39 10.01 0.000
Graph for publicat ion
<img s rc="MERDS_R_ code_SG_reA nalysis_20200828_files/figure-html/unnamed-chunk-40-1.png" width=“672” />

Presence total biomass of germinate

Diagnostic Graphs

fin_dataSG_biomass_seed_surv_pres_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_given_germ, root_association =="B")
nrow(fin_dataSG_biomass_seed_surv_pres_w_germ)
## [1] 26
#26
unique(fin_dataSG_biomass_seed_surv_pres_w_germ$soil_root)
## [1] L.B S.B
## Levels: L.B S.B L.R S.R
seed_biomas_model_pres= lmer((total_biomass)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_pres_w_germ)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(seed_biomas_model_pres))

## 25 75 
##  6 21
hist(resid(seed_biomas_model_pres))

shapiro.test(resid(seed_biomas_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_biomas_model_pres)
## W = 0.93832, p-value = 0.1224
#p-value = 0.1224

Raw Statistical output

anova(seed_biomas_model_pres)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## precip           0.43448 0.43448     1    22  147.58 3.139e-11 ***
## soil_root        0.44799 0.44799     1    22  152.17 2.336e-11 ***
## precip:soil_root 0.55107 0.55107     1    22  187.19 3.080e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(seed_biomas_model_pres, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean     SE    df lower.CL upper.CL
##  L.B       0.0664 0.0129  5.22   0.0338    0.099
##  S.B       0.3859 0.0262 12.75   0.3291    0.443
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate     SE   df t.ratio p.value
##  L.B - S.B   -0.319 0.0284 21.9 -11.252 <.0001 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger
emmeans(seed_biomas_model_pres, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean     SE   df  lower.CL upper.CL
##  L.B       0.0863 0.0180 12.8  0.047359   0.1252
##  S.B       0.0514 0.0251 20.1 -0.000969   0.1038
## 
## precip = D:
##  soil_root emmean     SE   df  lower.CL upper.CL
##  L.B       0.0466 0.0187 14.8  0.006715   0.0864
##  S.B       0.7203 0.0466 15.7  0.621443   0.8192
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate     SE   df t.ratio p.value
##  L.B - S.B   0.0348 0.0313 20.9   1.114 0.2780 
## 
## precip = D:
##  contrast  estimate     SE   df t.ratio p.value
##  L.B - S.B  -0.6737 0.0501 20.7 -13.448 <.0001 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Total biomass given germination
Df Resid.Df F.value P.value
1 22 147.58 0
1 22 152.17 0
1 22 187.19 0
### Or igin total b iomass of g erminated

Diagnostic Graphs

fin_dataSG_biomass_seed_surv_orig_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_given_germ, soil_status =="L")
nrow(fin_dataSG_biomass_seed_surv_orig_w_germ)
## [1] 38
#38
unique(fin_dataSG_biomass_seed_surv_orig_w_germ$soil_root)
## [1] L.R L.B
## Levels: L.B S.B L.R S.R
seed_biomas_model_orig= lmer(log(total_biomass)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_orig_w_germ)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(seed_biomas_model_orig))

## 12 52 
##  6 21
hist(resid(seed_biomas_model_orig))

shapiro.test(resid(seed_biomas_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(seed_biomas_model_orig)
## W = 0.97636, p-value = 0.5891
#p-value = 0.5891

Raw Statistical output

anova(seed_biomas_model_orig)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
## precip           8.4164  8.4164     1    34  8.7072 0.005705 **
## soil_root        0.3004  0.3004     1    34  0.3108 0.580839   
## precip:soil_root 0.2874  0.2874     1    34  0.2973 0.589143   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(seed_biomas_model_orig, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        -3.03 0.230 11.8    -3.53    -2.53
##  L.R        -2.85 0.246 11.4    -3.39    -2.31
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R   -0.181 0.333 32.4 -0.544  0.5901 
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.
emmeans(seed_biomas_model_orig, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        -2.64 0.320 24.0    -3.30    -1.98
##  L.R        -2.28 0.288 21.6    -2.88    -1.68
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        -3.42 0.335 26.6    -4.11    -2.73
##  L.R        -3.42 0.393 26.3    -4.23    -2.61
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R -0.35848 0.427 31.4 -0.840  0.4074 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R -0.00398 0.505 32.1 -0.008  0.9938 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Total biomass given germination
Df Resid.Df F.value P.value
1 34 8.71 0.006
1 34 0.31 0.581
1 34 0.30 0.589

Root:shoot ratio given germination

Diagnostic Graphs

#now root:shoot ratio
fin_dataSG_biomass_seed_surv_trt_given_germ$root_shoot=fin_dataSG_biomass_seed_surv_trt_given_germ$root_weight_g/fin_dataSG_biomass_seed_surv_trt_given_germ$shoot_weight_g
summary(fin_dataSG_biomass_seed_surv_trt_given_germ)
##   Plant_Number       date              Rdate              exp_days 
##  Min.   : 2.00   Length:45          Length:45          Min.   :27  
##  1st Qu.:23.00   Class :character   Class :character   1st Qu.:27  
##  Median :52.00   Mode  :character   Mode  :character   Median :27  
##  Mean   :46.49                                         Mean   :27  
##  3rd Qu.:66.00                                         3rd Qu.:27  
##  Max.   :89.00                                         Max.   :27  
##  num_germinates   volunteer_dicot  num_removed  raw_label        
##  Min.   :0.0000   Min.   :0       Min.   :0    Length:45         
##  1st Qu.:1.0000   1st Qu.:0       1st Qu.:0    Class :character  
##  Median :1.0000   Median :0       Median :0    Mode  :character  
##  Mean   :0.9778   Mean   :0       Mean   :0                      
##  3rd Qu.:1.0000   3rd Qu.:0       3rd Qu.:0                      
##  Max.   :1.0000   Max.   :0       Max.   :0                      
##  soil_status        root_association       block          precip         
##  Length:45          Length:45          Min.   :1.000   Length:45         
##  Class :character   Class :character   1st Qu.:2.000   Class :character  
##  Mode  :character   Mode  :character   Median :3.000   Mode  :character  
##                                        Mean   :3.156                     
##                                        3rd Qu.:4.000                     
##                                        Max.   :5.000                     
##   life_stage            trt              pot_w_germ     soil_root
##  Length:45          Length:45          Min.   :0.0000   L.B:19   
##  Class :character   Class :character   1st Qu.:1.0000   S.B: 7   
##  Mode  :character   Mode  :character   Median :1.0000   L.R:19   
##                                        Mean   :0.9778   S.R: 0   
##                                        3rd Qu.:1.0000            
##                                        Max.   :1.0000            
##  tot_num_germ_harvest    t0_germ         germinated root_weight_g   
##  Min.   :0.000        Min.   : 4.000   Min.   :1    Min.   :0.0021  
##  1st Qu.:1.000        1st Qu.: 4.000   1st Qu.:1    1st Qu.:0.0243  
##  Median :1.000        Median : 5.000   Median :1    Median :0.0507  
##  Mean   :1.556        Mean   : 6.044   Mean   :1    Mean   :0.1223  
##  3rd Qu.:2.000        3rd Qu.: 6.000   3rd Qu.:1    3rd Qu.:0.1022  
##  Max.   :4.000        Max.   :19.000   Max.   :1    Max.   :1.1619  
##  shoot_weight_g    total_biomass    notes_roots        notes_shoots      
##  Min.   :0.00050   Min.   :0.0026   Length:45          Length:45         
##  1st Qu.:0.00360   1st Qu.:0.0294   Class :character   Class :character  
##  Median :0.00640   Median :0.0612   Mode  :character   Mode  :character  
##  Mean   :0.00812   Mean   :0.1305                                        
##  3rd Qu.:0.01130   3rd Qu.:0.1116                                        
##  Max.   :0.02560   Max.   :1.1875                                        
##   sandy_root          surv_germ   root_shoot    
##  Length:45          Min.   :1   Min.   : 2.303  
##  Class :character   1st Qu.:1   1st Qu.: 4.168  
##  Mode  :character   Median :1   Median : 7.306  
##                     Mean   :1   Mean   :14.315  
##                     3rd Qu.:1   3rd Qu.:13.850  
##                     Max.   :1   Max.   :80.657
root_shoot_seed_model= lmer(log(root_shoot)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_trt_given_germ)
qqPlot(resid(root_shoot_seed_model))

## 51 52 
## 22 23
hist(resid(root_shoot_seed_model))

shapiro.test(resid(root_shoot_seed_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(root_shoot_seed_model)
## W = 0.97031, p-value = 0.2972
#0.2972

Raw Statistical output

anova(root_shoot_seed_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## precip           6.3079  6.3079     1 37.122  8.3663 0.006359 **
## soil_root        1.4238  0.7119     2 37.992  0.9442 0.397931   
## precip:soil_root 3.9101  1.9550     2 38.417  2.5930 0.087835 . 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(root_shoot_seed_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         2.06 0.229 10.5     1.55     2.57
##  S.B         2.56 0.406 28.2     1.73     3.39
##  L.R         2.35 0.240 11.1     1.83     2.88
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -0.504 0.432 38.0 -1.165  0.4808 
##  L.B - L.R   -0.293 0.293 36.5 -1.002  0.5802 
##  S.B - L.R    0.211 0.451 39.0  0.467  0.8869 
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(root_shoot_seed_model, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         2.00 0.300 22.1    1.382     2.63
##  S.B         1.54 0.412 34.2    0.701     2.37
##  L.R         2.08 0.276 18.5    1.506     2.66
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         2.11 0.313 24.7    1.469     2.76
##  S.B         3.59 0.683 36.3    2.203     4.97
##  L.R         2.62 0.359 28.2    1.887     3.36
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   0.4664 0.487 36.8  0.957  0.6082 
##  L.B - L.R  -0.0791 0.375 35.8 -0.211  0.9758 
##  S.B - L.R  -0.5455 0.466 35.8 -1.171  0.4779 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B  -1.4742 0.734 39.0 -2.010  0.1233 
##  L.B - L.R  -0.5076 0.445 36.4 -1.141  0.4954 
##  S.B - L.R   0.9666 0.783 38.4  1.235  0.4403 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Days to germination
Df Resid.Df F.value P.value
1 37.12 8.37 0.006
2 37.99 0.94 0.398
2 38.42 2.59 0.088

Graph for publication

Presence Root:shoot ratio given germination

Diagnostic Graphs

#now root:shoot ratio
fin_dataSG_biomass_seed_surv_pres_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_given_germ, root_association =="B")

root_shoot_seed_model_pres= lmer(log(root_shoot)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_pres_w_germ)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(root_shoot_seed_model_pres))

## 20 75 
##  2 21
hist(resid(root_shoot_seed_model_pres))

shapiro.test(resid(root_shoot_seed_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(root_shoot_seed_model_pres)
## W = 0.97141, p-value = 0.6602
#0.6602

Raw Statistical output

anova(root_shoot_seed_model_pres, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
## precip           5.2766  5.2766     1    22  9.7046 0.005042 **
## soil_root        1.1572  1.1572     1    22  2.1283 0.158731   
## precip:soil_root 4.8231  4.8231     1    22  8.8704 0.006934 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(root_shoot_seed_model_pres, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE    df lower.CL upper.CL
##  L.B         2.08 0.175  5.22     1.64     2.52
##  S.B         2.59 0.356 12.75     1.82     3.37
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -0.513 0.386 21.9 -1.331  0.1970 
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.
emmeans(root_shoot_seed_model_pres, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         2.06 0.244 12.8    1.529     2.59
##  S.B         1.52 0.341 20.1    0.811     2.23
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         2.11 0.254 14.8    1.564     2.65
##  S.B         3.67 0.633 15.7    2.324     5.01
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    0.535 0.425 20.9  1.258  0.2224 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -1.562 0.681 20.7 -2.294  0.0324 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Root:Shoot given germination
Df Resid.Df F.value P.value
1 22 9.70 0.005
1 22 2.13 0.159
1 22 8.87 0.007
### Or igin Root:sh oot ratio g iven germination

Diagnostic Graphs

#now root:shoot ratio
fin_dataSG_biomass_seed_surv_orig_w_germ=subset(fin_dataSG_biomass_seed_surv_trt_given_germ, soil_status =="L")

root_shoot_seed_model_orig= lmer(log(root_shoot)~precip*soil_root+(1|block), data= fin_dataSG_biomass_seed_surv_orig_w_germ)
qqPlot(resid(root_shoot_seed_model_orig))

## 51 52 
## 20 21
hist(resid(root_shoot_seed_model_orig))

shapiro.test(resid(root_shoot_seed_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(root_shoot_seed_model_orig)
## W = 0.96601, p-value = 0.2958
#0.2958

Raw Statistical output

anova(root_shoot_seed_model_orig, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
## precip           0.86205 0.86205     1 33.304  1.0003 0.3245
## soil_root        0.73422 0.73422     1 32.163  0.8519 0.3629
## precip:soil_root 0.41700 0.41700     1 31.393  0.4839 0.4918
emmeans(root_shoot_seed_model_orig, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE    df lower.CL upper.CL
##  L.B         2.07 0.238  9.66     1.53     2.60
##  L.R         2.35 0.250 10.07     1.79     2.91
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R   -0.285 0.314 31.8 -0.907  0.3711 
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.
emmeans(root_shoot_seed_model_orig, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         2.02 0.316 20.6     1.36     2.68
##  L.R         2.09 0.289 17.5     1.48     2.70
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         2.11 0.330 23.0     1.43     2.80
##  L.R         2.61 0.382 25.2     1.83     3.40
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R  -0.0709 0.402 31.0 -0.176  0.8612 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R  -0.4989 0.477 31.5 -1.046  0.3033 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Root:Shoot given germination
Df Resid.Df F.value P.value
1 33.30 1.00 0.324
1 32.16 0.85 0.363
1 31.39 0.48 0.492

Soil nitrate

Diagnostic Graphs

SG_inorg_N_trt$soil_root=with(SG_inorg_N_trt, interaction(soil_status,root_association))

SG_inorg_N_seed_trt= subset(SG_inorg_N_trt, life_stage=="S")
SG_soil_nit_model= lmer((ug_N_NO3_g_dry_soil)~precip*soil_root+(1|block), data= SG_inorg_N_seed_trt)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_soil_nit_model))

## [1] 1 2
hist(resid(SG_soil_nit_model))

shapiro.test(resid(SG_soil_nit_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_nit_model)
## W = 0.95913, p-value = 0.7402
#0.7402

Raw Statistical output

anova(SG_soil_nit_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## precip           11.4658 11.4658     1     7  38.967 0.0004273 ***
## soil_root         9.9131  4.9566     2     7  16.845 0.0021117 ** 
## precip:soil_root  8.8261  4.4131     2     7  14.998 0.0029466 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_soil_nit_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         5.98 0.344 7.00     5.17     6.80
##  S.B         3.33 0.369 7.00     2.46     4.21
##  L.R         4.68 0.257 5.49     4.04     5.32
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B     2.65 0.505 5.35  5.249  0.0064 
##  L.B - L.R     1.30 0.419 4.74  3.111  0.0621 
##  S.B - L.R    -1.35 0.450 5.49 -2.991  0.0603 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(SG_soil_nit_model, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B         3.57 0.344  7     2.76     4.38
##  S.B         3.31 0.436  7     2.28     4.35
##  L.R         3.91 0.271  7     3.27     4.55
## 
## precip = D:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B         8.40 0.690  7     6.77    10.03
##  S.B         3.35 0.664  7     1.78     4.92
##  L.R         5.45 0.436  7     4.42     6.48
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    0.254 0.571 6.37  0.445  0.8984 
##  L.B - L.R   -0.339 0.438 4.82 -0.773  0.7346 
##  S.B - L.R   -0.593 0.514 5.49 -1.153  0.5230 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    5.043 0.985 7.00  5.123  0.0034 
##  L.B - L.R    2.946 0.784 5.49  3.758  0.0254 
##  S.B - L.R   -2.098 0.848 7.00 -2.475  0.0953 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Days to germination
Df Resid.Df F.value P.value
1 29.56 4.28 0.047
1 28.58 8.82 0.006
1 30.33 1.85 0.183
Graph for publicat ion
<img s rc="MERDS_R_ code_SG_reA nalysis_20200828_files/figure-html/unnamed-chunk-60-1.png" width=“672” />

Soil ammonium

Diagnostic Graphs

SG_soil_amm_model= lmer((ug_N_NH4_g_dry_soil_negto0)~precip*soil_root+(1|block), data= SG_inorg_N_seed_trt)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_soil_amm_model))

## [1] 12 13
hist(resid(SG_soil_amm_model))

shapiro.test(resid(SG_soil_amm_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_amm_model)
## W = 0.60938, p-value = 7.899e-05
#p-value = 7.899e-05

Raw Statistical output

anova(SG_soil_amm_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip           0.2271 0.22709     1     7  0.2559 0.6285
## soil_root        5.3807 2.69034     2     7  3.0317 0.1126
## precip:soil_root 0.0893 0.04463     2     7  0.0503 0.9513
emmeans(SG_soil_amm_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        0.131 0.597 7.00   -1.281     1.54
##  S.B        1.851 0.641 7.00    0.335     3.37
##  L.R        0.279 0.446 5.49   -0.837     1.40
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -1.721 0.876 5.35 -1.964  0.2106 
##  L.B - L.R   -0.148 0.728 4.74 -0.204  0.9774 
##  S.B - L.R    1.572 0.781 5.49  2.013  0.1966 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(SG_soil_amm_model, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B       0.0406 0.597  7  -1.3716     1.45
##  S.B       1.7611 0.758  7  -0.0307     3.55
##  L.R       0.0088 0.471  7  -1.1050     1.12
## 
## precip = D:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B       0.2210 1.198  7  -2.6121     3.05
##  S.B       1.9418 1.154  7  -0.7864     4.67
##  L.R       0.5498 0.758  7  -1.2420     2.34
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B  -1.7205 0.992 6.37 -1.735  0.2644 
##  L.B - L.R   0.0318 0.761 4.82  0.042  0.9990 
##  S.B - L.R   1.7523 0.892 5.49  1.964  0.2085 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B  -1.7207 1.710 7.00 -1.006  0.5963 
##  L.B - L.R  -0.3288 1.361 5.49 -0.242  0.9685 
##  S.B - L.R   1.3919 1.472 7.00  0.946  0.6310 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

Graph for publication

## Warning: Removed 2 rows containing missing values (geom_text).

TRANSPLANT PORTION OF THE EXPERIEMENT BEGINING

Was there treatment effects on intial survival

Graph Was there a bias in initial height?

Diagnostic Graphs

SG_intial_height_model= lmer((int_height_cm)^(1/3)~precip*soil_root+(1|block), data= fin_dataSG_trans_surv_trt_planting)
qqPlot(resid(SG_intial_height_model))

## [1] 14  7
hist(resid(SG_intial_height_model))

shapiro.test(resid(SG_intial_height_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_intial_height_model)
## W = 0.93125, p-value = 0.0001383
#p-value = 0.0001383

Raw Statistical output

anova(SG_intial_height_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                    Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## precip           0.000081 0.0000813     1    80  0.0033 0.9546
## soil_root        0.044662 0.0223308     2    80  0.8961 0.4122
## precip:soil_root 0.060344 0.0301722     2    80  1.2108 0.3034

Formatted Anova table

Initial Hieght
Df Resid.Df F.value P.value
1 80 0.00 0.955
2 80 0.90 0.412
2 80 1.21 0.303
### To tal biomass

Diagnostic Graphs

trans_biomas_model= lmer(log(total_biomass_0+.001)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_trt)
qqPlot(resid(trans_biomas_model))

## [1] 29 41
hist(resid(trans_biomas_model))

shapiro.test(resid(trans_biomas_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(trans_biomas_model)
## W = 0.96619, p-value = 0.01918

Raw Statistical output

anova(trans_biomas_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## precip           31.948  31.948     1    80 13.4201 0.0004457 ***
## soil_root        23.301  11.651     2    80  4.8939 0.0098833 ** 
## precip:soil_root 15.880   7.940     2    80  3.3353 0.0406192 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(trans_biomas_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        -3.39 0.323 15.6    -4.08    -2.71
##  S.B        -2.48 0.323 15.6    -3.17    -1.80
##  L.R        -3.67 0.323 15.6    -4.36    -2.99
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE df t.ratio p.value
##  L.B - S.B   -0.909 0.398 80 -2.282  0.0641 
##  L.B - L.R    0.284 0.398 80  0.712  0.7570 
##  S.B - L.R    1.193 0.398 80  2.994  0.0101 
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(trans_biomas_model, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        -3.16 0.429 38.4    -4.03   -2.294
##  S.B        -1.30 0.429 38.4    -2.17   -0.431
##  L.R        -3.30 0.429 38.4    -4.17   -2.434
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        -3.62 0.429 38.4    -4.49   -2.754
##  S.B        -3.67 0.429 38.4    -4.53   -2.799
##  L.R        -4.05 0.429 38.4    -4.92   -3.181
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE df t.ratio p.value
##  L.B - S.B  -1.8632 0.563 80 -3.307  0.0040 
##  L.B - L.R   0.1403 0.563 80  0.249  0.9664 
##  S.B - L.R   2.0036 0.563 80  3.556  0.0018 
## 
## precip = D:
##  contrast  estimate    SE df t.ratio p.value
##  L.B - S.B   0.0449 0.563 80  0.080  0.9965 
##  L.B - L.R   0.4272 0.563 80  0.758  0.7295 
##  S.B - L.R   0.3823 0.563 80  0.678  0.7767 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Total biomass
Df Resid.Df F.value P.value
1 80 13.42 0.000
2 80 4.89 0.010
2 80 3.34 0.041

Graph for publication

Presence at total biomass

Diagnostic Graphs

data_SG_biomass_trans_surv_pres=subset(data_SG_biomass_trans_surv_trt, root_association =="B")

trans_biomas_model_pres= lmer((total_biomass_0+.001)^(1/4)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_pres)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(trans_biomas_model_pres))

## 85 34 
## 55 19
hist(resid(trans_biomas_model_pres))

shapiro.test(resid(trans_biomas_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(trans_biomas_model_pres)
## W = 0.97499, p-value = 0.254
#p-value = 0.254

Raw Statistical output

anova(trans_biomas_model_pres, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## precip           0.38241 0.38241     1    56 13.2458 0.0005969 ***
## soil_root        0.29851 0.29851     1    56 10.3396 0.0021637 ** 
## precip:soil_root 0.25587 0.25587     1    56  8.8628 0.0042933 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(trans_biomas_model_pres, pairwise~soil_root)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        0.455 0.031 14.9    0.389    0.521
##  S.B        0.596 0.031 14.9    0.530    0.662
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate     SE df t.ratio p.value
##  L.B - S.B   -0.141 0.0439 52 -3.216  0.0022 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger
emmeans(trans_biomas_model_pres, pairwise~soil_root|precip)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B        0.469 0.0439 37.8    0.380    0.558
##  S.B        0.741 0.0439 37.8    0.652    0.830
## 
## precip = D:
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B        0.440 0.0439 37.8    0.351    0.529
##  S.B        0.451 0.0439 37.8    0.362    0.540
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE df t.ratio p.value
##  L.B - S.B  -0.2717 0.062 52 -4.379  0.0001 
## 
## precip = D:
##  contrast  estimate    SE df t.ratio p.value
##  L.B - S.B  -0.0105 0.062 52 -0.169  0.8667 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Total biomass
Df Resid.Df F.value P.value
1 56 13.25 0.001
1 56 10.34 0.002
1 56 8.86 0.004

Origin at total biomass

Diagnostic Graphs

data_SG_biomass_trans_surv_orig=subset(data_SG_biomass_trans_surv_trt, soil_status =="L")
nrow(data_SG_biomass_trans_surv_orig)
## [1] 60
trans_biomas_model_orig= lmer((total_biomass_0+0.001)^(1/4)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_orig)
qqPlot(resid(trans_biomas_model_orig))

## [1]  2 29
hist(resid(trans_biomas_model_orig))

shapiro.test(resid(trans_biomas_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(trans_biomas_model_orig)
## W = 0.97214, p-value = 0.1856
#p-value = 0.1856

Raw Statistical output

anova(trans_biomas_model_orig)
## Type III Analysis of Variance Table with Satterthwaite's method
##                     Sum Sq   Mean Sq NumDF DenDF F value Pr(>F)
## precip           0.0212028 0.0212028     1    52  1.0133 0.3188
## soil_root        0.0111201 0.0111201     1    52  0.5314 0.4693
## precip:soil_root 0.0010927 0.0010927     1    52  0.0522 0.8201
emmeans(trans_biomas_model_orig, pairwise~soil_root)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean     SE  df lower.CL upper.CL
##  L.B        0.455 0.0313 9.4    0.384    0.525
##  L.R        0.428 0.0313 9.4    0.357    0.498
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate     SE df t.ratio p.value
##  L.B - L.R   0.0272 0.0373 52 0.729   0.4693 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger
emmeans(trans_biomas_model_orig, pairwise~soil_root|precip)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        0.469 0.041 23.3    0.385    0.554
##  L.R        0.451 0.041 23.3    0.366    0.535
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        0.440 0.041 23.3    0.356    0.525
##  L.R        0.404 0.041 23.3    0.320    0.489
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate     SE df t.ratio p.value
##  L.B - L.R   0.0187 0.0528 52 0.354   0.7249 
## 
## precip = D:
##  contrast  estimate     SE df t.ratio p.value
##  L.B - L.R   0.0358 0.0528 52 0.677   0.5014 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Total biomass
Df Resid.Df F.value P.value
1 52 1.01 0.319
1 52 0.53 0.469
1 52 0.05 0.820

Root:shoot ratio

Diagnostic Graphs

root_shoot_model_na= lmer(log(root_shoot)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_trt_na)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(root_shoot_model_na))

## 53 45 
## 43 35
hist(resid(root_shoot_model_na))

shapiro.test(resid(root_shoot_model_na))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(root_shoot_model_na)
## W = 0.94233, p-value = 0.001287
#p-value = 0.001287
plot(root_shoot_model_na)

Raw Statistical output

anova(root_shoot_model_na)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip           2.3613 2.36129     1    74  1.4419 0.2337
## soil_root        1.0009 0.50045     2    74  0.3056 0.7376
## precip:soil_root 1.5907 0.79534     2    74  0.4857 0.6172
emmeans(root_shoot_model_na, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         1.13 0.243 26.7    0.628     1.62
##  S.B         1.03 0.255 29.7    0.511     1.55
##  L.R         1.31 0.257 28.1    0.782     1.83
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   0.0942 0.352 70.7  0.267  0.9614 
##  L.B - L.R  -0.1818 0.354 71.9 -0.514  0.8648 
##  S.B - L.R  -0.2759 0.361 71.3 -0.765  0.7258 
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(root_shoot_model_na, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        0.814 0.330 54.4    0.152     1.48
##  S.B        1.054 0.330 54.4    0.392     1.72
##  L.R        1.077 0.330 54.4    0.415     1.74
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        1.438 0.356 58.5    0.726     2.15
##  S.B        1.010 0.389 61.7    0.232     1.79
##  L.R        1.539 0.393 56.9    0.752     2.33
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B  -0.2403 0.467 70.0 -0.514  0.8647 
##  L.B - L.R  -0.2630 0.467 70.0 -0.563  0.8402 
##  S.B - L.R  -0.0227 0.467 70.0 -0.049  0.9987 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   0.4286 0.527 71.2  0.814  0.6959 
##  L.B - L.R  -0.1006 0.531 73.0 -0.189  0.9804 
##  S.B - L.R  -0.5292 0.550 72.1 -0.962  0.6031 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Root:Shoot Ratio
Df Resid.Df F.value P.value
1 74 1.44 0.234
2 74 0.31 0.738
2 74 0.49 0.617

Graph for publication

Presence Root:shoot ratio

Diagnostic Graphs

data_SG_biomass_trans_surv_pres_na=subset(data_SG_biomass_trans_surv_trt_na, root_association =="B")

root_shoot_model_pres_na= lmer((root_shoot)^(1/4)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_pres_na)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(root_shoot_model_pres_na))

## 25 45 
##  9 24
hist(resid(root_shoot_model_pres_na))

shapiro.test(resid(root_shoot_model_pres_na))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(root_shoot_model_pres_na)
## W = 0.94371, p-value = 0.01335
#p-value = 0.01335
plot(root_shoot_model_pres_na)

Raw Statistical output

anova(root_shoot_model_pres_na)
## Type III Analysis of Variance Table with Satterthwaite's method
##                    Sum Sq  Mean Sq NumDF DenDF F value Pr(>F)
## precip           0.175840 0.175840     1    50  1.1108 0.2970
## soil_root        0.009263 0.009263     1    50  0.0585 0.8099
## precip:soil_root 0.250414 0.250414     1    50  1.5818 0.2143
emmeans(root_shoot_model_pres_na, pairwise~soil_root)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean     SE   df lower.CL upper.CL
##  L.B         1.38 0.0756 13.6     1.22     1.55
##  S.B         1.36 0.0795 15.4     1.19     1.53
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - S.B   0.0264 0.11 46.7 0.241   0.8107 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger
emmeans(root_shoot_model_pres_na, pairwise~soil_root|precip)
## Warning in ref_grid(object, ...): There are unevaluated constants in the response formula
## Auto-detection of the response transformation may be incorrect
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         1.26 0.103 32.5     1.05     1.47
##  S.B         1.37 0.103 32.5     1.16     1.58
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         1.51 0.111 35.8     1.28     1.73
##  S.B         1.35 0.121 38.5     1.10     1.59
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -0.111 0.145 46.0 -0.763  0.4492 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    0.164 0.164 47.2  0.997  0.3238 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Root:Shoot Ratio
Df Resid.Df F.value P.value
1 50 1.11 0.297
1 50 0.06 0.810
1 50 1.58 0.214

Origin Root:shoot ratio

Diagnostic Graphs

data_SG_biomass_trans_surv_orig_na=subset(data_SG_biomass_trans_surv_trt_na,soil_status =="L")

root_shoot_model_orig_na= lmer(log(root_shoot)~precip*soil_root+(1|block), data= data_SG_biomass_trans_surv_orig_na)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(root_shoot_model_orig_na))

## 53 25 
## 32 20
hist(resid(root_shoot_model_orig_na))

shapiro.test(resid(root_shoot_model_orig_na))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(root_shoot_model_orig_na)
## W = 0.94863, p-value = 0.02169
#p-value = 0.02169
plot(root_shoot_model_orig_na)

Raw Statistical output

anova(root_shoot_model_orig_na)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip           3.9134  3.9134     1    50  2.4860 0.1212
## soil_root        0.4389  0.4389     1    50  0.2788 0.5998
## precip:soil_root 0.0875  0.0875     1    50  0.0556 0.8145
emmeans(root_shoot_model_orig_na, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        0.814 0.324 32.3    0.155     1.47
##  L.R        1.077 0.324 32.3    0.417     1.74
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        1.438 0.350 35.6    0.729     2.15
##  L.R        1.539 0.388 34.2    0.750     2.33
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE df t.ratio p.value
##  L.B - L.R   -0.263 0.458 46 -0.574  0.5688 
## 
## precip = D:
##  contrast  estimate    SE df t.ratio p.value
##  L.B - L.R   -0.101 0.523 49 -0.192  0.8484 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Root:Shoot Ratio
Df Resid.Df F.value P.value
1 50 2.49 0.121
1 50 0.28 0.600
1 50 0.06 0.815

Final survival

data_SG_biomass_trans_surv_trt$surv=data_SG_biomass_trans_surv_trt$total_biomass
data_SG_biomass_trans_surv_trt$surv[is.na(data_SG_biomass_trans_surv_trt$surv)]=0
data_SG_biomass_trans_surv_trt$surv[data_SG_biomass_trans_surv_trt$surv!=0]=1

Diagnostic Graphs

Raw Statistical output

Graph for publication

Survival of the transplant

Final survival in the drought treatment only since all pots had live transs in the ambient treatments

Diagnostic Graphs

data_SG_biomass_trans_surv_drought=subset(data_SG_biomass_trans_surv_trt, precip=="D")
drought_surv_model= glmer(surv~soil_root+(1|block), data= data_SG_biomass_trans_surv_drought, family = binomial)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.0139762 (tol = 0.002, component 1)
qqPlot(resid(drought_surv_model))

## [1] 29 41
hist(resid(drought_surv_model))

Raw Statistical output

Anova(drought_surv_model,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv
##             Chisq Df Pr(>Chisq)    
## (Intercept) 88736  1  < 2.2e-16 ***
## soil_root   15916  2  < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(drought_surv_model, pairwise~soil_root)
## $emmeans
##  soil_root emmean      SE  df asymp.LCL asymp.UCL
##  L.B         2.15 0.00716 Inf     2.133      2.16
##  S.B         1.19 0.44169 Inf     0.323      2.05
##  L.R         1.19 0.44169 Inf     0.323      2.05
## 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B 9.58e-01 0.442 Inf 2.168   0.0767 
##  L.B - L.R 9.58e-01 0.442 Inf 2.168   0.0767 
##  S.B - L.R 2.16e-05 0.883 Inf 0.000   1.0000 
## 
## Results are given on the log odds ratio (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Survival
Df Chi.Sq P.value
(Intercept) 1 88735.85 0
soil_root 2 15916.28 0

Presence survival of the transplant

Diagnostic Graphs

data_SG_biomass_trans_surv_drought_pres=subset(data_SG_biomass_trans_surv_drought, root_association =="B")

drought_surv_model_pres= glmer(surv~soil_root+(1|block), data= data_SG_biomass_trans_surv_drought_pres, family = binomial)
qqPlot(resid(drought_surv_model_pres))

## 29 16 
## 14  1
hist(resid(drought_surv_model_pres))

Raw Statistical output

Anova(drought_surv_model_pres,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv
##              Chisq Df Pr(>Chisq)   
## (Intercept) 7.2643  1   0.007034 **
## soil_root   0.8025  1   0.370340   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(drought_surv_model_pres, pairwise~soil_root)
## $emmeans
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B         1.89 0.815 Inf     0.288      3.48
##  S.B         1.02 0.619 Inf    -0.192      2.23
## 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B    0.865 0.966 Inf 0.896   0.3703 
## 
## Results are given on the log odds ratio (not the response) scale.

Formatted Anova table

Survival
Df Chi.Sq P.value
(Intercept) 1 7.26 0.007
soil_root 1 0.80 0.370

Origin survival of the transplant

Diagnostic Graphs

data_SG_biomass_trans_surv_drought_orig=subset(data_SG_biomass_trans_surv_drought, soil_status =="L")

drought_surv_model_orig= glmer(surv~soil_root+(1|block), data= data_SG_biomass_trans_surv_drought_orig, family = binomial)
qqPlot(resid(drought_surv_model_orig))

## [1] 29 16
hist(resid(drought_surv_model_orig))

Raw Statistical output

Anova(drought_surv_model_orig,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv
##              Chisq Df Pr(>Chisq)  
## (Intercept) 4.9500  1    0.02609 *
## soil_root   0.8716  1    0.35052  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(drought_surv_model_orig, pairwise~soil_root)
## $emmeans
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B         2.12 0.996 Inf     0.173      4.08
##  L.R         1.17 0.791 Inf    -0.377      2.73
## 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE  df z.ratio p.value
##  L.B - L.R     0.95 1.02 Inf 0.934   0.3505 
## 
## Results are given on the log odds ratio (not the response) scale.

Formatted Anova table

Survival
Df Chi.Sq P.value
(Intercept) 1 4.95 0.026
soil_root 1 0.87 0.351

Root traits

Diagnostic Graphs

SRL_trans_model= lmer((SRL_length_drymass)~precip*soil_root+(1|block), data= SG_roottraits_trt)
qqPlot(resid(SRL_trans_model))

## [1] 18  8
hist(resid(SRL_trans_model))

shapiro.test(resid(SRL_trans_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SRL_trans_model)
## W = 0.97902, p-value = 0.8396

Raw Statistical output

anova(SRL_trans_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                     Sum Sq   Mean Sq NumDF  DenDF F value  Pr(>F)  
## precip           119509598 119509598     1 18.017  5.5697 0.02975 *
## soil_root         33012605  16506303     2 17.454  0.7693 0.47843  
## precip:soil_root 174500103  87250052     2 17.454  4.0663 0.03551 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SRL_trans_model, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean   SE   df lower.CL upper.CL
##  L.B        14406 2145 20.6     9939    18873
##  S.B        10446 2145 20.6     5979    14913
##  L.R        11348 2145 20.6     6882    15815
## 
## precip = D:
##  soil_root emmean   SE   df lower.CL upper.CL
##  L.B        12476 2145 20.6     8009    16942
##  S.B        21317 2844 21.0    15402    27232
##  L.R        15338 2430 20.8    10282    20394
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - S.B     3960 2930 17.1  1.352  0.3872 
##  L.B - L.R     3058 2930 17.1  1.044  0.5604 
##  S.B - L.R     -902 2930 17.1 -0.308  0.9492 
## 
## precip = D:
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - S.B    -8841 3474 18.2 -2.545  0.0504 
##  L.B - L.R    -2862 3144 17.6 -0.910  0.6411 
##  S.B - L.R     5979 3593 17.7  1.664  0.2462 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

SRL
Df Resid.Df F.value P.value
1 18.02 5.57 0.030
2 17.45 0.77 0.478
2 17.45 4.07 0.036
Graph for publicat ion

Presence specific root length

Diagnostic Graphs

SG_roottraits_pres=subset(SG_roottraits_trt, root_association =="B")

SRL_trans_model_pres= lmer((SRL_length_drymass)~precip*soil_root+(1|block), data= SG_roottraits_pres)
qqPlot(resid(SRL_trans_model_pres))

## 18  8 
##  9  4
hist(resid(SRL_trans_model_pres))

shapiro.test(resid(SRL_trans_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SRL_trans_model_pres)
## W = 0.97446, p-value = 0.876

Raw Statistical output

anova(SRL_trans_model_pres)
## Type III Analysis of Variance Table with Satterthwaite's method
##                     Sum Sq   Mean Sq NumDF  DenDF F value  Pr(>F)  
## precip            95886515  95886515     1 10.131  4.8407 0.05209 .
## soil_root         31805805  31805805     1 10.131  1.6057 0.23345  
## precip:soil_root 188806702 188806702     1 10.131  9.5317 0.01133 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SRL_trans_model_pres, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean   SE   df lower.CL upper.CL
##  L.B        14406 2304 12.0     9387    19424
##  S.B        10446 2304 12.0     5428    15464
## 
## precip = D:
##  soil_root emmean   SE   df lower.CL upper.CL
##  L.B        12476 2304 12.0     7457    17494
##  S.B        21949 2987 13.8    15532    28366
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - S.B     3960 2815 10.0  1.407  0.1897 
## 
## precip = D:
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - S.B    -9473 3397 10.8 -2.789  0.0180 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

SRL
Df Resid.Df F.value P.value
1 10.13 4.84 0.052
1 10.13 1.61 0.233
1 10.13 9.53 0.011

Origin specific root length

Diagnostic Graphs

SG_roottraits_orig=subset(SG_roottraits_trt,soil_status =="L")
SRL_trans_model_orig= lmer((SRL_length_drymass)~precip*soil_root+(1|block), data= SG_roottraits_orig)
qqPlot(resid(SRL_trans_model_orig))

## 18  8 
## 15  8
hist(resid(SRL_trans_model_orig))

shapiro.test(resid(SRL_trans_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SRL_trans_model_orig)
## W = 0.968, p-value = 0.7359

Raw Statistical output

anova(SRL_trans_model_orig)
## Type III Analysis of Variance Table with Satterthwaite's method
##                    Sum Sq  Mean Sq NumDF  DenDF F value Pr(>F)
## precip            4777217  4777217     1 11.588  0.1724 0.6856
## soil_root           66735    66735     1 11.588  0.0024 0.9617
## precip:soil_root 40567718 40567718     1 11.588  1.4637 0.2504
emmeans(SRL_trans_model_orig, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean   SE df lower.CL upper.CL
##  L.B        14406 2390 15     9312    19500
##  L.R        11348 2390 15     6254    16443
## 
## precip = D:
##  soil_root emmean   SE df lower.CL upper.CL
##  L.B        12476 2390 15     7381    17570
##  L.R        15295 2741 15     9452    21138
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - L.R     3058 3330 11.0  0.918  0.3781 
## 
## precip = D:
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - L.R    -2819 3590 11.6 -0.785  0.4480 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

SRL
Df Resid.Df F.value P.value
1 11.59 0.17 0.686
1 11.59 0.00 0.962
1 11.59 1.46 0.250

Root length density

Diagnostic Graphs

RLD_trans_model= lmer((RLD_length_volume)~precip*soil_root+(1|block), data= SG_roottraits_trt)
qqPlot(resid(RLD_trans_model))

## [1] 23  1
hist(resid(RLD_trans_model))

Raw Statistical output

anova(RLD_trans_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## precip           0.4102  0.4102     1 16.865  5.0491  0.038322 *  
## soil_root        6.3528  3.1764     2 16.061 39.0983 6.734e-07 ***
## precip:soil_root 1.2403  0.6201     2 16.061  7.6332  0.004677 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(RLD_trans_model, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        0.548 0.130 20.9    0.277    0.818
##  S.B        2.071 0.130 20.9    1.801    2.342
##  L.R        0.487 0.130 20.9    0.216    0.757
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        0.472 0.130 20.9    0.202    0.743
##  S.B        1.195 0.173 21.0    0.835    1.555
##  L.R        0.683 0.148 20.9    0.376    0.990
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B  -1.5238 0.180 17.1 -8.453  <.0001 
##  L.B - L.R   0.0609 0.180 17.1  0.338  0.9393 
##  S.B - L.R   1.5847 0.180 17.1  8.791  <.0001 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B  -0.7229 0.214 18.4 -3.384  0.0086 
##  L.B - L.R  -0.2109 0.193 17.7 -1.091  0.5318 
##  S.B - L.R   0.5119 0.221 17.8  2.315  0.0794 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

RLD
Df Resid.Df F.value P.value
1 16.86 5.05 0.038
2 16.06 39.10 0.000
2 16.06 7.63 0.005
<img s rc="MERDS_R_ code_SG_reA nalysis_20200828_files/figure-html/unnamed-chunk-119-1.png" width=“672” />

Rhizosheath

Diagnostic Graphs

rhizosheath_model= lmer(log(RhizosheathSoil_DryRoots_g +1)~precip*soil_root+(1|block), data= SG_roottraits_trt)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(rhizosheath_model))

## [1] 16  2
hist(resid(rhizosheath_model))

shapiro.test(resid(rhizosheath_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(rhizosheath_model)
## W = 0.97019, p-value = 0.6068
#0.6068

Raw Statistical output

anova(rhizosheath_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value  Pr(>F)  
## precip           0.1008 0.10081     1    21  0.1016 0.75304  
## soil_root        5.1477 2.57383     2    21  2.5945 0.09841 .
## precip:soil_root 0.1843 0.09213     2    21  0.0929 0.91169  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Initial Hieght
Df Resid.Df F.value P.value
1 21 0.10 0.753
2 21 2.59 0.098
2 21 0.09 0.912

Graph for publication

Presence Rhizosheath

Diagnostic Graphs

SG_roottraits_pres=subset(SG_roottraits_trt, root_association =="B")

rhizosheath_model_pres= lmer(log(RhizosheathSoil_DryRoots_g)~precip*soil_root+(1|block), data= SG_roottraits_pres)
qqPlot(resid(rhizosheath_model_pres))

## 19 24 
## 10 15
hist(resid(rhizosheath_model_pres))

shapiro.test(resid(rhizosheath_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(rhizosheath_model_pres)
## W = 0.95982, p-value = 0.5982
#0.5982

Raw Statistical output

anova(rhizosheath_model_pres, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)  
## precip           0.59058 0.59058     1 10.064  1.4693 0.2531  
## soil_root        1.72540 1.72540     1 10.064  4.2926 0.0649 .
## precip:soil_root 0.03550 0.03550     1 10.064  0.0883 0.7724  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Initial Hieght
Df Resid.Df F.value P.value
1 10.06 1.47 0.253
1 10.06 4.29 0.065
1 10.06 0.09 0.772
### Or igin Rhizosh eath

Diagnostic Graphs

SG_roottraits_orig=subset(SG_roottraits_trt,soil_status =="L")

rhizosheath_model_orig= lmer(log(RhizosheathSoil_DryRoots_g+1)~precip*soil_root+(1|block), data= SG_roottraits_orig)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(rhizosheath_model_orig))

## 16  2 
## 13  2
hist(resid(rhizosheath_model_orig))

shapiro.test(resid(rhizosheath_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(rhizosheath_model_orig)
## W = 0.96907, p-value = 0.7577
#0.7577

Raw Statistical output

anova(rhizosheath_model_orig, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                   Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## precip           0.02583 0.02583     1    15  0.0212 0.8862
## soil_root        1.90925 1.90925     1    15  1.5649 0.2301
## precip:soil_root 0.15779 0.15779     1    15  0.1293 0.7241

Formatted Anova table

Initial Hieght
Df Resid.Df F.value P.value
1 15 0.02 0.886
1 15 1.56 0.230
1 15 0.13 0.724

Soil nitrogen

Diagnostic Graphs

SG_soil_nit_trans_model= lmer((ug_N_NO3_g_dry_soil)~precip*soil_root+(1|block), data= SG_inorg_N_trans_trt)
qqPlot(resid(SG_soil_nit_trans_model))

## 17 15 
##  4  2
hist(resid(SG_soil_nit_trans_model))

shapiro.test(resid(SG_soil_nit_trans_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_nit_trans_model)
## W = 0.96397, p-value = 0.4755

Raw Statistical output

anova(SG_soil_nit_trans_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## precip           5.3802  5.3802     1 16.868 15.2231 0.001162 **
## soil_root        6.4455  3.2227     2 17.249  9.1186 0.001986 **
## precip:soil_root 3.2720  1.6360     2 16.841  4.6289 0.025001 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_soil_nit_trans_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         4.42 0.230 14.5     3.93     4.92
##  S.B         3.44 0.201 13.6     3.01     3.87
##  L.R         4.52 0.234 16.0     4.03     5.02
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   0.9848 0.289 17.4  3.413  0.0086 
##  L.B - L.R  -0.0989 0.307 16.8 -0.323  0.9445 
##  S.B - L.R  -1.0837 0.292 16.9 -3.715  0.0047 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(SG_soil_nit_trans_model, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         3.86 0.312 19.8     3.21     4.51
##  S.B         3.45 0.275 19.6     2.87     4.02
##  L.R         3.69 0.275 19.6     3.11     4.26
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         4.99 0.312 19.8     4.34     5.64
##  S.B         3.43 0.275 19.6     2.86     4.01
##  L.R         5.36 0.365 20.0     4.59     6.12
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    0.409 0.403 16.8  1.015  0.5780 
##  L.B - L.R    0.168 0.403 16.8  0.415  0.9099 
##  S.B - L.R   -0.242 0.376 16.1 -0.643  0.7986 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    1.560 0.403 16.8  3.867  0.0034 
##  L.B - L.R   -0.365 0.462 16.8 -0.791  0.7133 
##  S.B - L.R   -1.925 0.446 17.4 -4.315  0.0012 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Soil nitrate
Df Resid.Df F.value P.value
1 16.87 15.22 0.001
2 17.25 9.12 0.002
2 16.84 4.63 0.025
Graph for publicat ion
<img s rc="MERDS_R_ code_SG_reA nalysis_20200828_files/figure-html/unnamed-chunk-134-1.png" width=“672” />

Presence nitrate transplant

Diagnostic Graphs

SG_inorg_N_trans_pres=subset(SG_inorg_N_trans_trt, root_association =="B")
SG_soil_nit_trans_model_pres= lmer((ug_N_NO3_g_dry_soil)~precip*soil_root+(1|block), data= SG_inorg_N_trans_pres)
qqPlot(resid(SG_soil_nit_trans_model_pres))

## 17 19 
##  1  3
hist(resid(SG_soil_nit_trans_model_pres))

shapiro.test(resid(SG_soil_nit_trans_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_nit_trans_model_pres)
## W = 0.89259, p-value = 0.04276

Raw Statistical output

anova(SG_soil_nit_trans_model_pres, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## precip           1.3872  1.3872     1 13.998  5.5927 0.033015 * 
## soil_root        4.2187  4.2187     1 13.998 17.0078 0.001033 **
## precip:soil_root 1.4715  1.4715     1 13.998  5.9324 0.028831 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_soil_nit_trans_model_pres, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         4.41 0.183 10.2     4.01     4.82
##  S.B         3.44 0.157 10.2     3.09     3.79
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    0.974 0.242 11.4 4.031   0.0018 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger
emmeans(SG_soil_nit_trans_model_pres, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B         3.85 0.254 14     3.30     4.39
##  S.B         3.45 0.223 14     2.97     3.93
## 
## precip = D:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B         4.98 0.254 14     4.44     5.53
##  S.B         3.43 0.223 14     2.95     3.91
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    0.399 0.338 10.8 1.180   0.2632 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    1.550 0.338 10.8 4.585   0.0008 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Soil nitrate
Df Resid.Df F.value P.value
1 14 5.59 0.033
1 14 17.01 0.001
1 14 5.93 0.029

Origin nitrate transplant

Diagnostic Graphs

SG_inorg_N_trans_orig=subset(SG_inorg_N_trans_trt,soil_status =="L")
SG_soil_nit_trans_model_orig= lmer((ug_N_NO3_g_dry_soil)~precip*soil_root+(1|block), data= SG_inorg_N_trans_orig)
qqPlot(resid(SG_soil_nit_trans_model_orig))

## 17 19 
##  4  6
hist(resid(SG_soil_nit_trans_model_orig))

shapiro.test(resid(SG_soil_nit_trans_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_nit_trans_model_orig)
## W = 0.96308, p-value = 0.7179

Raw Statistical output

anova(SG_soil_nit_trans_model_orig, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## precip           7.5837  7.5837     1 9.2345 14.0014 0.004408 **
## soil_root        0.0385  0.0385     1 9.2345  0.0711 0.795559   
## precip:soil_root 0.2890  0.2890     1 9.2345  0.5335 0.483251   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_soil_nit_trans_model_orig, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         4.43 0.302 7.39     3.72     5.14
##  L.R         4.53 0.307 8.59     3.83     5.23
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R     -0.1 0.383 8.99 -0.262  0.7992 
## 
## Results are averaged over the levels of: precip 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger
emmeans(SG_soil_nit_trans_model_orig, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         3.86 0.399 11.7     2.99     4.74
##  L.R         3.69 0.351 11.6     2.92     4.46
## 
## precip = D:
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B         5.00 0.399 11.7     4.13     5.87
##  L.R         5.37 0.473 11.9     4.34     6.40
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R    0.175 0.503 9.05  0.347  0.7362 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - L.R   -0.376 0.579 8.95 -0.649  0.5328 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Soil nitrate
Df Resid.Df F.value P.value
1 9.23 14.00 0.004
1 9.23 0.07 0.796
1 9.23 0.53 0.483

Amonnium

Diagnostic Graphs

SG_soil_amm_trans_model= lmer(log(ug_N_NH4_g_dry_soil_negto0+0.007)~precip*soil_root+(1|block), data= SG_inorg_N_trans_trt)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_soil_amm_trans_model))

## 20 28 
##  7 15
hist(resid(SG_soil_amm_trans_model))

shapiro.test(resid(SG_soil_amm_trans_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_amm_trans_model)
## W = 0.96033, p-value = 0.3981

Raw Statistical output

anova(SG_soil_amm_trans_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## precip            7.010  7.0097     1    20  5.3624   0.03131 *  
## soil_root        43.257 21.6285     2    20 16.5458 5.755e-05 ***
## precip:soil_root  8.962  4.4812     2    20  3.4281   0.05246 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_soil_amm_trans_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root  emmean    SE   df lower.CL upper.CL
##  L.B       -2.8384 0.415 16.2   -3.718   -1.959
##  S.B        0.0188 0.362 16.2   -0.747    0.784
##  L.R       -2.3990 0.428 17.7   -3.299   -1.499
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.007) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -2.857 0.551 17.5 -5.188  0.0002 
##  L.B - L.R   -0.439 0.588 17.0 -0.747  0.7396 
##  S.B - L.R    2.418 0.560 17.1  4.317  0.0013 
## 
## Results are averaged over the levels of: precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(SG_soil_amm_trans_model, pairwise~soil_root|precip)
## $emmeans
## precip = A:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B        -2.56 0.580 20  -3.7734  -1.3554
##  S.B        -1.10 0.511 20  -2.1707  -0.0376
##  L.R        -3.13 0.511 20  -4.2015  -2.0683
## 
## precip = D:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B        -3.11 0.580 20  -4.3214  -1.9034
##  S.B         1.14 0.511 20   0.0751   2.2083
##  L.R        -1.66 0.686 20  -3.0934  -0.2327
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.007) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -1.460 0.773 16.9 -1.889  0.1723 
##  L.B - L.R    0.571 0.773 16.9  0.738  0.7447 
##  S.B - L.R    2.031 0.723 16.2  2.808  0.0317 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -4.254 0.773 16.9 -5.504  0.0001 
##  L.B - L.R   -1.449 0.888 17.1 -1.633  0.2593 
##  S.B - L.R    2.805 0.855 17.7  3.279  0.0112 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Initial Hieght
Df Resid.Df F.value P.value
1 20 5.36 0.031
2 20 16.55 0.000
2 20 3.43 0.052

Graph for publication

Presence Amonnium

Diagnostic Graphs

SG_inorg_N_trans_pres=subset(SG_inorg_N_trans_trt, root_association =="B")

SG_soil_amm_trans_model_pres= lmer(sqrt(ug_N_NH4_g_dry_soil_negto0)~precip*soil_root+(1|block), data= SG_inorg_N_trans_pres)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_soil_amm_trans_model_pres))

## 24 22 
##  8  6
hist(resid(SG_soil_amm_trans_model_pres))

shapiro.test(resid(SG_soil_amm_trans_model_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_amm_trans_model_pres)
## W = 0.93074, p-value = 0.2001

Raw Statistical output

anova(SG_soil_amm_trans_model_pres, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                  Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## precip           1.3408  1.3408     1    14  18.813 0.0006824 ***
## soil_root        3.8066  3.8066     1    14  53.410  3.86e-06 ***
## precip:soil_root 1.7746  1.7746     1    14  24.899 0.0001982 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_soil_amm_trans_model_pres, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B        0.317 0.136 14   0.0245    0.609
##  S.B        0.610 0.119 14   0.3543    0.866
## 
## precip = D:
##  soil_root emmean    SE df lower.CL upper.CL
##  L.B        0.234 0.136 14  -0.0581    0.526
##  S.B        1.792 0.119 14   1.5354    2.048
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -0.294 0.181 10.8 -1.620  0.1338 
## 
## precip = D:
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B   -1.557 0.181 10.8 -8.596  <.0001 
## 
## Note: contrasts are still on the sqrt scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Initial Hieght
Df Resid.Df F.value P.value
1 14 18.81 0.001
1 14 53.41 0.000
1 14 24.90 0.000

Origin ammonium transplant

Diagnostic Graphs

SG_inorg_N_trans_orig=subset(SG_inorg_N_trans_trt,soil_status =="L")

SG_soil_amm_trans_model_orig= lmer((ug_N_NH4_g_dry_soil_negto0)~precip*soil_root+(1|block), data= SG_inorg_N_trans_orig)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_soil_amm_trans_model_orig))

## 31 16 
## 13  3
hist(resid(SG_soil_amm_trans_model_orig))

shapiro.test(resid(SG_soil_amm_trans_model_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_amm_trans_model_orig)
## W = 0.9063, p-value = 0.1014

Raw Statistical output

anova(SG_soil_amm_trans_model_orig, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                    Sum Sq  Mean Sq NumDF DenDF F value Pr(>F)
## precip           0.006632 0.006632     1    12  0.4456 0.5171
## soil_root        0.001692 0.001692     1    12  0.1137 0.7418
## precip:soil_root 0.033168 0.033168     1    12  2.2285 0.1613
emmeans(SG_soil_amm_trans_model_orig, pairwise~soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean     SE df lower.CL upper.CL
##  L.B       0.1422 0.0618 12  0.00754    0.277
##  L.R       0.0705 0.0546 12 -0.04833    0.189
## 
## precip = D:
##  soil_root emmean     SE df lower.CL upper.CL
##  L.B       0.0910 0.0618 12 -0.04363    0.226
##  L.R       0.2045 0.0745 12  0.04209    0.367
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate     SE   df t.ratio p.value
##  L.B - L.R   0.0717 0.0824 9.12  0.869  0.4070 
## 
## precip = D:
##  contrast  estimate     SE   df t.ratio p.value
##  L.B - L.R  -0.1135 0.0958 9.27 -1.184  0.2657 
## 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Initial Hieght
Df Resid.Df F.value P.value
1 12 0.45 0.517
1 12 0.11 0.742
1 12 2.23 0.161

VWC Analyses

Diagnostic Graphs

SG_soil_VWC_model= lmer((percent_soil_moisture_dry_weight)~precip*soil_root*life_stage+(1|block), data= SG_inorg_N_trt)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_soil_VWC_model))

## [1] 20 33
hist(resid(SG_soil_VWC_model))

shapiro.test(resid(SG_soil_VWC_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_soil_VWC_model)
## W = 0.96851, p-value = 0.3377
#0.3377

Raw Statistical output

anova(SG_soil_VWC_model, type=3)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
## precip                      307.117 307.117     1    27  7.9808 0.008782 **
## soil_root                    41.285  20.643     2    27  0.5364 0.590938   
## life_stage                  135.010 135.010     1    27  3.5084 0.071923 . 
## precip:soil_root             22.074  11.037     2    27  0.2868 0.752914   
## precip:life_stage             0.405   0.405     1    27  0.0105 0.919080   
## soil_root:life_stage         13.084   6.542     2    27  0.1700 0.844555   
## precip:soil_root:life_stage   3.642   1.821     2    27  0.0473 0.953857   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_soil_VWC_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  soil_root emmean   SE   df lower.CL upper.CL
##  L.B        11.96 2.17 21.0     7.45     16.5
##  S.B         8.86 2.20 23.1     4.32     13.4
##  L.R        10.60 1.80 16.9     6.81     14.4
## 
## Results are averaged over the levels of: precip, life_stage 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - S.B     3.10 3.06 25.0  1.011  0.5771 
##  L.B - L.R     1.35 2.74 23.6  0.493  0.8753 
##  S.B - L.R    -1.74 2.82 24.7 -0.618  0.8117 
## 
## Results are averaged over the levels of: precip, life_stage 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(SG_soil_VWC_model, pairwise~soil_root|precip)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## precip = A:
##  soil_root emmean   SE   df lower.CL upper.CL
##  L.B        15.40 2.43 24.2   10.396     20.4
##  S.B        11.00 2.68 25.7    5.489     16.5
##  L.R        14.83 2.09 24.1   10.524     19.1
## 
## precip = D:
##  soil_root emmean   SE   df lower.CL upper.CL
##  L.B         8.51 3.68 26.4    0.958     16.1
##  S.B         6.73 3.59 26.5   -0.639     14.1
##  L.R         6.37 2.90 26.4    0.408     12.3
## 
## Results are averaged over the levels of: life_stage 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the ( (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## precip = A:
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - S.B    4.408 3.63 25.7  1.213  0.4564 
##  L.B - L.R    0.570 3.18 24.2  0.179  0.9825 
##  S.B - L.R   -3.838 3.39 24.7 -1.133  0.5033 
## 
## precip = D:
##  contrast  estimate   SE   df t.ratio p.value
##  L.B - S.B    1.784 5.20 26.9  0.343  0.9373 
##  L.B - L.R    2.136 4.53 24.1  0.472  0.8852 
##  S.B - L.R    0.352 4.66 26.5  0.075  0.9969 
## 
## Results are averaged over the levels of: life_stage 
## Note: contrasts are still on the ( scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Soil VWC
Df Resid.Df F.value P.value
1 27 7.98 0.009
2 27 0.54 0.591
1 27 3.51 0.072
2 27 0.29 0.753
1 27 0.01 0.919
2 27 0.17 0.845
2 27 0.05 0.954

Start Soil nitrogen

Soil nitrate

Diagnostic Graphs

SG_start_inorg_N$soil_root=with(SG_start_inorg_N, interaction(soil_status,root_association))

SG_Soil_nit_start= lm((ug_N_NO3_g_dry_soil)~soil_root+block, data= SG_start_inorg_N)
qqPlot(resid(SG_Soil_nit_start))

## [1] 14  7
hist(resid(SG_Soil_nit_start))

shapiro.test(resid(SG_Soil_nit_start))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_Soil_nit_start)
## W = 0.94653, p-value = 0.4716

Raw Statistical output

Anova(SG_Soil_nit_start,type = 3)
## Anova Table (Type III tests)
## 
## Response: (ug_N_NO3_g_dry_soil)
##              Sum Sq Df F value    Pr(>F)    
## (Intercept) 124.752  1 49.7581 2.116e-05 ***
## soil_root     3.977  2  0.7931    0.4767    
## block         3.893  1  1.5529    0.2386    
## Residuals    27.579 11                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Soil Nitrate
Df F.value P.value
(Intercept) 1 49.76 0.000
soil_root 2 0.79 0.477
block 1 1.55 0.239
Residuals 11 NA NA

Graph for publication

Presence Start SOil nitrate

Diagnostic Graphs

SG_start_inorg_N_pres=subset(SG_start_inorg_N, root_association =="B")

SG_Soil_nit_start_pres= lm((ug_N_NO3_g_dry_soil)~soil_root+block, data= SG_start_inorg_N_pres)
qqPlot(resid(SG_Soil_nit_start_pres))

## 14  1 
##  9  1
hist(resid(SG_Soil_nit_start_pres))

shapiro.test(resid(SG_Soil_nit_start_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_Soil_nit_start_pres)
## W = 0.93286, p-value = 0.4766

Raw Statistical output

Anova(SG_Soil_nit_start_pres,type = 3)
## Anova Table (Type III tests)
## 
## Response: (ug_N_NO3_g_dry_soil)
##             Sum Sq Df F value    Pr(>F)    
## (Intercept) 75.236  1 32.3655 0.0007437 ***
## soil_root    2.846  1  1.2242 0.3051005    
## block        5.728  1  2.4643 0.1604494    
## Residuals   16.272  7                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Soil Nitrate
Df F.value P.value
(Intercept) 1 32.37 0.001
soil_root 1 1.22 0.305
block 1 2.46 0.160
Residuals 7 NA NA

Origin Start SOil nitrate

Diagnostic Graphs

SG_start_inorg_N_orig=subset(SG_start_inorg_N, soil_status =="L")

SG_Soil_nit_start_orig= lm((ug_N_NO3_g_dry_soil)^3~soil_root+block, data= SG_start_inorg_N_orig)
qqPlot(resid(SG_Soil_nit_start_orig))

## [1] 3 7
hist(resid(SG_Soil_nit_start_orig))

shapiro.test(resid(SG_Soil_nit_start_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_Soil_nit_start_orig)
## W = 0.82833, p-value = 0.03194
boxCox(SG_Soil_nit_start_orig)

Raw Statistical output

Anova(SG_Soil_nit_start_orig,type = 3)
## Anova Table (Type III tests)
## 
## Response: (ug_N_NO3_g_dry_soil)^3
##             Sum Sq Df F value  Pr(>F)  
## (Intercept) 496747  1  7.4104 0.02967 *
## soil_root   104794  1  1.5633 0.25136  
## block         2696  1  0.0402 0.84675  
## Residuals   469239  7                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Soil Nitrate
Df F.value P.value
(Intercept) 1 7.41 0.030
soil_root 1 1.56 0.251
block 1 0.04 0.847
Residuals 7 NA NA

Soil ammonium

Diagnostic Graphs

SG_start_inorg_N$soil_root=with(SG_start_inorg_N, interaction(soil_status,root_association))

SG_Soil_amm_start= lm(log(ug_N_NH4_g_dry_soil)~soil_root+block, data= SG_start_inorg_N)
qqPlot(resid(SG_Soil_amm_start))

## [1]  4 10
hist(resid(SG_Soil_amm_start))

shapiro.test(resid(SG_Soil_amm_start))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_Soil_amm_start)
## W = 0.93105, p-value = 0.2829
boxCox(SG_Soil_amm_start)

Raw Statistical output

Anova(SG_Soil_amm_start,type = 3)
## Anova Table (Type III tests)
## 
## Response: log(ug_N_NH4_g_dry_soil)
##              Sum Sq Df F value    Pr(>F)    
## (Intercept)  9.9251  1 98.8744 7.823e-07 ***
## soil_root   12.6065  2 62.7930 9.615e-07 ***
## block        0.0374  1  0.3728    0.5539    
## Residuals    1.1042 11                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Soil Ammonium
Df F.value P.value
(Intercept) 1 98.87 0.000
soil_root 2 62.79 0.000
block 1 0.37 0.554
Residuals 11 NA NA

Graph for publication

Presence Start SOil Ammonium

Diagnostic Graphs

SG_start_inorg_N_pres=subset(SG_start_inorg_N, root_association =="B")

SG_Soil_amm_start_pres= lm((ug_N_NH4_g_dry_soil)~soil_root+block, data= SG_start_inorg_N_pres)
qqPlot(resid(SG_Soil_amm_start_pres))

## 14 15 
##  9 10
hist(resid(SG_Soil_amm_start_pres))

shapiro.test(resid(SG_Soil_amm_start_pres))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_Soil_amm_start_pres)
## W = 0.87313, p-value = 0.1087
boxCox(SG_Soil_amm_start_pres)

Raw Statistical output

Anova(SG_Soil_amm_start_pres,type = 3)
## Anova Table (Type III tests)
## 
## Response: (ug_N_NH4_g_dry_soil)
##              Sum Sq Df F value    Pr(>F)    
## (Intercept)  247.50  1  9.3004 0.0185923 *  
## soil_root   1423.78  1 53.5011 0.0001608 ***
## block         44.24  1  1.6626 0.2382233    
## Residuals    186.29  7                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Soil Ammonium
Df F.value P.value
(Intercept) 1 9.30 0.019
soil_root 1 53.50 0.000
block 1 1.66 0.238
Residuals 7 NA NA

Origin Start SOil Ammonium

Diagnostic Graphs

SG_start_inorg_N_orig=subset(SG_start_inorg_N, soil_status =="L")

SG_Soil_amm_start_orig= lm((ug_N_NH4_g_dry_soil)^-1~soil_root+block, data= SG_start_inorg_N_orig)
qqPlot(resid(SG_Soil_amm_start_orig))

## [1]  4 10
hist(resid(SG_Soil_amm_start_orig))

shapiro.test(resid(SG_Soil_amm_start_orig))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_Soil_amm_start_orig)
## W = 0.96944, p-value = 0.8856
boxCox(SG_Soil_amm_start_orig)

Raw Statistical output

Anova(SG_Soil_amm_start_orig,type = 3)
## Anova Table (Type III tests)
## 
## Response: (ug_N_NH4_g_dry_soil)^-1
##               Sum Sq Df F value   Pr(>F)   
## (Intercept) 0.128490  1  18.228 0.003704 **
## soil_root   0.000268  1   0.038 0.850978   
## block       0.000000  1   0.000 0.996817   
## Residuals   0.049344  7                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Soil Ammonium
Df F.value P.value
(Intercept) 1 18.23 0.004
soil_root 1 0.04 0.851
block 1 0.00 0.997
Residuals 7 NA NA

Microbial Biomass

Soil ammonium

Diagnostic Graphs

SG_Soil_DOC_mod= lm(log(unfumigated_DOC_concentration_ugC_gsoil)~soil_root+block, data= SG_MBC_DOC_trt)
qqPlot(resid(SG_Soil_DOC_mod))

## [1] 6 2
hist(resid(SG_Soil_DOC_mod))

shapiro.test(resid(SG_Soil_DOC_mod))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_Soil_DOC_mod)
## W = 0.9533, p-value = 0.6131
boxCox(SG_Soil_DOC_mod)

Raw Statistical output

Anova(SG_Soil_DOC_mod,type = 3)
## Anova Table (Type III tests)
## 
## Response: log(unfumigated_DOC_concentration_ugC_gsoil)
##              Sum Sq Df F value    Pr(>F)    
## (Intercept) 16.5452  1 38.7139 9.857e-05 ***
## soil_root    2.2287  2  2.6074    0.1226    
## block        0.0006  1  0.0014    0.9712    
## Residuals    4.2737 10                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Formatted Anova table

Soil Ammonium
Df F.value P.value
(Intercept) 1 38.71 0.000
soil_root 2 2.61 0.123
block 1 0.00 0.971
Residuals 10 NA NA

Graph for publication

Combined Seed and Seedling Analyses

Survival

Diagnostic Graphs

#data_SG_biomass_trans_surv_drought=subset(data_SG_biomass_trans_surv_trt, precip=="D")
SG_combin_surv_model= glmer(surv~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_biomass_surv_trt_comb, family = binomial)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 3 negative eigenvalues
qqPlot(resid(SG_combin_surv_model))

## 24 29 
## 14 88
hist(resid(SG_combin_surv_model))

shapiro.test(resid(SG_combin_surv_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_surv_model)
## W = 0.83916, p-value = 1.729e-11

Raw Statistical output

Anova(SG_combin_surv_model,type = 3)
## Warning in vcov.merMod(mod, complete = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX

## Warning in vcov.merMod(mod, complete = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv
##                             Chisq Df Pr(>Chisq)
## (Intercept)                     0  1     0.9949
## soil_root                       0  2     1.0000
## precip                          0  1     0.9960
## life_stage                      0  1     0.9959
## soil_root:precip                0  2     1.0000
## soil_root:life_stage            0  2     1.0000
## precip:life_stage               0  1     0.9961
## soil_root:precip:life_stage     0  2     1.0000
emmeans(SG_combin_surv_model, pairwise~soil_root)
## Warning in vcov.merMod(object, correlation = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean   SE  df asymp.LCL asymp.UCL
##  L.B         6.67 1656 Inf     -3239      3253
##  S.B         5.37 1762 Inf     -3449      3460
##  L.R         6.44 1555 Inf     -3042      3055
## 
## Results are averaged over the levels of: precip, life_stage 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate   SE  df z.ratio p.value
##  L.B - S.B    1.297 2418 Inf 0.001   1.0000 
##  L.B - L.R    0.225 2272 Inf 0.000   1.0000 
##  S.B - L.R   -1.071 2351 Inf 0.000   1.0000 
## 
## Results are averaged over the levels of: precip, life_stage 
## Results are given on the log odds ratio (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

## Warning in vcov.merMod(mod, complete = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX

## Warning in vcov.merMod(mod, complete = FALSE): variance-covariance matrix computed from finite-difference Hessian is
## not positive definite or contains NA values: falling back to var-cov estimated from RX
Survival
Df Chi.Sq P.value
(Intercept) 1 0 0.995
soil_root 2 0 1.000
precip 1 0 0.996
life_stage 1 0 0.996
soil_root:precip 2 0 1.000
soil_root:life_stage 2 0 1.000
precip:life_stage 1 0 0.996
soil_root:precip:life_stage 2 0 1.000

Focus in on Drought treatment

Diagnostic Graphs

data_SG_biomass_surv_trt_comb_drought=subset(data_SG_biomass_surv_trt_comb, precip=="D")
SG_combin_surv_drought_model= glmer(surv~soil_root*life_stage+(1|block/life_stage), data= data_SG_biomass_surv_trt_comb_drought, family = binomial)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_combin_surv_drought_model))

## 24 29 
## 14 53
hist(resid(SG_combin_surv_drought_model))

shapiro.test(resid(SG_combin_surv_drought_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_surv_drought_model)
## W = 0.76715, p-value = 4.209e-09

Raw Statistical output

Anova(SG_combin_surv_drought_model,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv
##                       Chisq Df Pr(>Chisq)  
## (Intercept)          6.5558  1    0.01045 *
## soil_root            6.1744  2    0.04563 *
## life_stage           0.1295  1    0.71900  
## soil_root:life_stage 3.7279  2    0.15506  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_surv_drought_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B       2.1866 0.778 Inf     0.662      3.71
##  S.B       0.0958 0.668 Inf    -1.214      1.41
##  L.R       1.7439 0.761 Inf     0.253      3.23
## 
## Results are averaged over the levels of: life_stage 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B    2.091 0.896 Inf  2.335  0.0511 
##  L.B - L.R    0.443 0.912 Inf  0.486  0.8781 
##  S.B - L.R   -1.648 0.886 Inf -1.860  0.1504 
## 
## Results are averaged over the levels of: life_stage 
## Results are given on the log odds ratio (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates

Formatted Anova table

Survival
Df Chi.Sq P.value
(Intercept) 1 6.56 0.010
soil_root 2 6.17 0.046
life_stage 1 0.13 0.719
soil_root:life_stage 2 3.73 0.155
### Presence combined s urviva l
Diagnostic Graphs
data_SG_biomass_surv_trt_comb_drought_pres=subset(data_SG_biomass_surv_trt_comb_drought, root_association =="B")
SG_combin_surv_drought_pres_model= glmer(surv~soil_root*life_stage+(1|block/life_stage), data= data_SG_biomass_surv_trt_comb_drought_pres, family = binomial)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_combin_surv_drought_pres_model))

## 24 16 
##  6 17
hist(resid(SG_combin_surv_drought_pres_model))

shapiro.test(resid(SG_combin_surv_drought_pres_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_surv_drought_pres_model)
## W = 0.74597, p-value = 1.487e-07

Raw Statistical output

Anova(SG_combin_surv_drought_pres_model,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv
##                       Chisq Df Pr(>Chisq)   
## (Intercept)          6.9271  1    0.00849 **
## soil_root            5.0619  1    0.02446 * 
## life_stage           0.6846  1    0.40800   
## soil_root:life_stage 1.4832  1    0.22328   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_surv_drought_pres_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B        2.035 0.650 Inf     0.761      3.31
##  S.B        0.159 0.522 Inf    -0.864      1.18
## 
## Results are averaged over the levels of: life_stage 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - S.B     1.88 0.834 Inf 2.250   0.0245 
## 
## Results are averaged over the levels of: life_stage 
## Results are given on the log odds ratio (not the response) scale.

Formatted Anova table

Survival
Df Chi.Sq P.value
(Intercept) 1 6.93 0.008
soil_root 1 5.06 0.024
life_stage 1 0.68 0.408
soil_root:life_stage 1 1.48 0.223

Origin combined survival

Diagnostic Graphs

data_SG_biomass_surv_trt_comb_drought_orig=subset(data_SG_biomass_surv_trt_comb_drought, soil_status =="L")
SG_combin_surv_drought_orig_model= glmer(surv~soil_root*life_stage+(1|block/life_stage), data= data_SG_biomass_surv_trt_comb_drought_orig, family = binomial)
qqPlot(resid(SG_combin_surv_drought_orig_model))

## 29 24 
## 47 14
hist(resid(SG_combin_surv_drought_orig_model))

shapiro.test(resid(SG_combin_surv_drought_orig_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_surv_drought_orig_model)
## W = 0.61185, p-value = 4.955e-10

Raw Statistical output

Anova(SG_combin_surv_drought_orig_model,type = 3)
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: surv
##                        Chisq Df Pr(>Chisq)   
## (Intercept)          10.0712  1   0.001506 **
## soil_root             0.4678  1   0.493979   
## life_stage            0.3427  1   0.558290   
## soil_root:life_stage  0.0938  1   0.759389   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_surv_drought_orig_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE  df asymp.LCL asymp.UCL
##  L.B         2.23 0.813 Inf     0.637      3.82
##  L.R         1.58 0.712 Inf     0.188      2.98
## 
## Results are averaged over the levels of: life_stage 
## Results are given on the logit (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df z.ratio p.value
##  L.B - L.R    0.646 0.944 Inf 0.684   0.4940 
## 
## Results are averaged over the levels of: life_stage 
## Results are given on the log odds ratio (not the response) scale.

Formatted Anova table

Survival
Df Chi.Sq P.value
(Intercept) 1 10.07 0.002
soil_root 1 0.47 0.494
life_stage 1 0.34 0.558
soil_root:life_stage 1 0.09 0.759

Biomass

Diagnostic Graphs

SG_combin_biomass_model= lmer(log(total_biomass+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_biomass_trt_comb)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_combin_biomass_model))

## 29 41 
## 74 86
hist(resid(SG_combin_biomass_model))

shapiro.test(resid(SG_combin_biomass_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_biomass_model)
## W = 0.97333, p-value = 0.009383

Raw Statistical output

anova(SG_combin_biomass_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## soil_root                   16.1994  8.0997     2 120.64  4.2800 0.016003 * 
## precip                       4.8969  4.8969     1 119.83  2.5876 0.110338   
## life_stage                   8.0818  8.0818     1 122.02  4.2705 0.040894 * 
## soil_root:precip             3.1145  1.5573     2 121.19  0.8229 0.441607   
## soil_root:life_stage         1.5159  0.7580     2 120.64  0.4005 0.670864   
## precip:life_stage           12.5200 12.5200     1 119.83  6.6157 0.011330 * 
## soil_root:precip:life_stage 26.2502 13.1251     2 121.19  6.9355 0.001407 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_biomass_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE   df lower.CL upper.CL
##  L.B        -3.21 0.244 12.6    -3.74    -2.68
##  S.B        -2.21 0.357 39.6    -2.93    -1.49
##  L.R        -3.24 0.250 13.3    -3.78    -2.70
## 
## Results are averaged over the levels of: precip, life_stage 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE  df t.ratio p.value
##  L.B - S.B  -1.0068 0.383 121 -2.627  0.0261 
##  L.B - L.R   0.0259 0.291 118  0.089  0.9956 
##  S.B - L.R   1.0327 0.394 120  2.622  0.0265 
## 
## Results are averaged over the levels of: precip, life_stage 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 3 estimates
emmeans(SG_combin_biomass_model, pairwise~life_stage)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant   -2.59 0.292 16.74    -3.21    -1.97
##  seedling    -3.18 0.198  4.93    -3.69    -2.67
## 
## Results are averaged over the levels of: soil_root, precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.593 0.297 5.87 1.996   0.0940 
## 
## Results are averaged over the levels of: soil_root, precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.
emmeans(SG_combin_biomass_model, pairwise~precip*life_stage)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  precip life_stage emmean    SE   df lower.CL upper.CL
##  A      germinant   -2.73 0.322 26.6    -3.39    -2.07
##  D      germinant   -2.45 0.437 57.6    -3.33    -1.58
##  A      seedling    -2.59 0.245 11.6    -3.12    -2.05
##  D      seedling    -3.78 0.245 11.6    -4.31    -3.24
## 
## Results are averaged over the levels of: soil_root 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                  estimate    SE    df t.ratio p.value
##  A,germinant - D,germinant   -0.275 0.497 119.8 -0.552  0.9458 
##  A,germinant - A,seedling    -0.140 0.357  13.4 -0.391  0.9789 
##  A,germinant - D,seedling     1.052 0.357  13.4  2.943  0.0482 
##  D,germinant - A,seedling     0.135 0.463  28.7  0.291  0.9912 
##  D,germinant - D,seedling     1.327 0.463  28.7  2.862  0.0369 
##  A,seedling - D,seedling      1.192 0.290 115.7  4.109  0.0004 
## 
## Results are averaged over the levels of: soil_root 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 4 estimates
emmeans(SG_combin_biomass_model, pairwise~life_stage|soil_root|precip)
## $emmeans
## soil_root = L.B, precip = A:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant  -2.657 0.464  74.8    -3.58   -1.733
##  seedling   -3.161 0.380  52.7    -3.92   -2.399
## 
## soil_root = S.B, precip = A:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant  -3.218 0.642 109.2    -4.49   -1.947
##  seedling   -1.298 0.380  52.7    -2.06   -0.536
## 
## soil_root = L.R, precip = A:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant  -2.304 0.424  64.2    -3.15   -1.457
##  seedling   -3.301 0.380  52.7    -4.06   -2.540
## 
## soil_root = L.B, precip = D:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant  -3.412 0.485  82.6    -4.38   -2.448
##  seedling   -3.621 0.380  52.7    -4.38   -2.859
## 
## soil_root = S.B, precip = D:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant  -0.642 1.058 102.9    -2.74    1.456
##  seedling   -3.666 0.380  52.7    -4.43   -2.904
## 
## soil_root = L.R, precip = D:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant  -3.301 0.556  90.3    -4.41   -2.197
##  seedling   -4.048 0.380  52.7    -4.81   -3.287
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## soil_root = L.B, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.504 0.568 59.1  0.887  0.3786 
## 
## soil_root = S.B, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling   -1.920 0.721 86.3 -2.663  0.0092 
## 
## soil_root = L.R, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.997 0.537 53.4  1.858  0.0687 
## 
## soil_root = L.B, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.209 0.586 64.1  0.356  0.7229 
## 
## soil_root = S.B, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    3.024 1.108 89.6  2.729  0.0076 
## 
## soil_root = L.R, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.747 0.646 68.9  1.157  0.2513 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Total biomass
Df Resid.Df F.value P.value
soil_root 2 120.63 4.28 0.016
precip 1 119.83 2.59 0.110
life_stage 1 122.02 4.27 0.041
soil_root:precip 2 121.20 0.82 0.442
soil_root:life_stage 2 120.63 0.40 0.671
precip:life_stage 1 119.83 6.62 0.011
soil_root:precip:life_stage 2 121.20 6.94 0.001
### Presence Combined Biomass

Diagnostic Graphs

data_SG_biomass_trt_comb_pres=subset(data_SG_biomass_trt_comb, root_association =="B")
SG_combin_biomass_pres_model= lmer(log(total_biomass+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_biomass_trt_comb_pres)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_combin_biomass_pres_model))

## 41 36 
## 52 47
hist(resid(SG_combin_biomass_pres_model))

shapiro.test(resid(SG_combin_biomass_pres_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_biomass_pres_model)
## W = 0.95636, p-value = 0.005474

Raw Statistical output

anova(SG_combin_biomass_pres_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
## soil_root                   14.6111 14.6111     1 76.129  7.7962 0.0066170 ** 
## precip                       0.6526  0.6526     1 76.407  0.3482 0.5568529    
## life_stage                   3.1065  3.1065     1 77.802  1.6576 0.2017495    
## soil_root:precip             2.0195  2.0195     1 77.573  1.0776 0.3024683    
## soil_root:life_stage         0.2408  0.2408     1 76.129  0.1285 0.7209959    
## precip:life_stage           19.0950 19.0950     1 76.407 10.1887 0.0020518 ** 
## soil_root:precip:life_stage 24.0017 24.0017     1 77.573 12.8068 0.0005993 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_biomass_pres_model, pairwise~soil_root)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  soil_root emmean    SE    df lower.CL upper.CL
##  L.B        -3.22 0.235  8.46    -3.75    -2.68
##  S.B        -2.17 0.357 27.22    -2.91    -1.44
## 
## Results are averaged over the levels of: precip, life_stage 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast  estimate    SE   df t.ratio p.value
##  L.B - S.B    -1.04 0.387 76.3 -2.694  0.0087 
## 
## Results are averaged over the levels of: precip, life_stage 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.
emmeans(SG_combin_biomass_pres_model, pairwise~precip*life_stage)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  precip life_stage emmean    SE   df lower.CL upper.CL
##  A      germinant   -2.94 0.403 37.1    -3.76   -2.122
##  D      germinant   -1.97 0.606 47.9    -3.18   -0.748
##  A      seedling    -2.23 0.275 13.5    -2.82   -1.637
##  D      seedling    -3.64 0.275 13.5    -4.24   -3.051
## 
## Results are averaged over the levels of: soil_root 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                  estimate    SE   df t.ratio p.value
##  A,germinant - D,germinant   -0.973 0.694 76.7 -1.402  0.5021 
##  A,germinant - A,seedling    -0.709 0.460 14.4 -1.542  0.4399 
##  A,germinant - D,seedling     0.705 0.460 14.4  1.531  0.4454 
##  D,germinant - A,seedling     0.263 0.645 24.7  0.408  0.9765 
##  D,germinant - D,seedling     1.677 0.645 24.7  2.601  0.0689 
##  A,seedling - D,seedling      1.414 0.353 71.1  4.000  0.0009 
## 
## Results are averaged over the levels of: soil_root 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale. 
## P value adjustment: tukey method for comparing a family of 4 estimates
emmeans(SG_combin_biomass_pres_model, pairwise~life_stage|soil_root|precip)
## $emmeans
## soil_root = L.B, precip = A:
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant  -2.661 0.461 49.7    -3.59   -1.734
##  seedling   -3.161 0.372 36.6    -3.92   -2.407
## 
## soil_root = S.B, precip = A:
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant  -3.217 0.642 70.8    -4.50   -1.938
##  seedling   -1.298 0.372 36.6    -2.05   -0.544
## 
## soil_root = L.B, precip = D:
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant  -3.422 0.482 55.1    -4.39   -2.456
##  seedling   -3.621 0.372 36.6    -4.37   -2.867
## 
## soil_root = S.B, precip = D:
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant  -0.511 1.094 62.9    -2.70    1.675
##  seedling   -3.666 0.372 36.6    -4.42   -2.912
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## soil_root = L.B, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.501 0.570 28.5  0.879  0.3867 
## 
## soil_root = S.B, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling   -1.919 0.723 46.7 -2.653  0.0109 
## 
## soil_root = L.B, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.200 0.586 31.5  0.341  0.7357 
## 
## soil_root = S.B, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    3.155 1.144 52.0  2.759  0.0080 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Total biomass
Df Resid.Df F.value P.value
soil_root 1 76.13 7.80 0.007
precip 1 76.41 0.35 0.557
life_stage 1 77.80 1.66 0.202
soil_root:precip 1 77.57 1.08 0.302
soil_root:life_stage 1 76.13 0.13 0.721
precip:life_stage 1 76.41 10.19 0.002
soil_root:precip:life_stage 1 77.57 12.81 0.001

Origin Combined Biomass

Diagnostic Graphs

data_SG_biomass_trt_comb_orig=subset(data_SG_biomass_trt_comb, soil_status =="L")
SG_combin_biomass_orig_model= lmer(log(total_biomass+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_biomass_trt_comb_orig)
qqPlot(resid(SG_combin_biomass_orig_model))

## 29 16 
## 67 54
hist(resid(SG_combin_biomass_orig_model))

shapiro.test(resid(SG_combin_biomass_orig_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_biomass_orig_model)
## W = 0.98373, p-value = 0.2687

Raw Statistical output

anova(SG_combin_biomass_orig_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF  DenDF F value   Pr(>F)   
## soil_root                    0.0170  0.0170     1 85.439  0.0100 0.920743   
## precip                      12.2652 12.2652     1 87.205  7.1861 0.008784 **
## life_stage                   6.5403  6.5403     1  4.368  3.8319 0.115906   
## soil_root:precip             0.4565  0.4565     1 84.367  0.2674 0.606410   
## soil_root:life_stage         1.4816  1.4816     1 85.439  0.8681 0.354118   
## precip:life_stage            0.4221  0.4221     1 87.205  0.2473 0.620225   
## soil_root:precip:life_stage  0.0001  0.0001     1 84.367  0.0000 0.995601   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_biomass_orig_model, pairwise~precip)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  precip emmean    SE    df lower.CL upper.CL
##  A       -2.85 0.224  8.49    -3.36    -2.34
##  D       -3.59 0.243 11.13    -4.12    -3.05
## 
## Results are averaged over the levels of: soil_root, life_stage 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE   df t.ratio p.value
##  A - D       0.741 0.281 86.7 2.636   0.0099 
## 
## Results are averaged over the levels of: soil_root, life_stage 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Total biomass
Df Resid.Df F.value P.value
soil_root 1 85.44 0.01 0.921
precip 1 87.20 7.19 0.009
life_stage 1 4.37 3.83 0.116
soil_root:precip 1 84.37 0.27 0.606
soil_root:life_stage 1 85.44 0.87 0.354
precip:life_stage 1 87.20 0.25 0.620
soil_root:precip:life_stage 1 84.37 0.00 0.996

Root:shoot ratio

Diagnostic Graphs

SG_combin_root_shoot_model= lmer(log(root_shoot+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_root_shoot_trt_comb)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_combin_root_shoot_model))

## 531  45 
##  88  80
hist(resid(SG_combin_root_shoot_model))

shapiro.test(resid(SG_combin_root_shoot_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_root_shoot_model)
## W = 0.97959, p-value = 0.05557

Raw Statistical output

anova(SG_combin_root_shoot_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)    
## soil_root                    1.231   0.615     2   113  0.4566  0.63462    
## precip                       8.680   8.680     1   113  6.4396  0.01252 *  
## life_stage                  31.625  31.625     1   113 23.4632 4.08e-06 ***
## soil_root:precip             1.711   0.856     2   113  0.6349  0.53189    
## soil_root:life_stage         1.276   0.638     2   113  0.4735  0.62405    
## precip:life_stage            1.687   1.687     1   113  1.2517  0.26560    
## soil_root:precip:life_stage  6.372   3.186     2   113  2.3637  0.09871 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_root_shoot_model, pairwise~life_stage)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant    2.34 0.217 21.53    1.890     2.79
##  seedling     1.16 0.132  5.06    0.817     1.50
## 
## Results are averaged over the levels of: soil_root, precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling     1.18 0.254 6.13 4.661   0.0033 
## 
## Results are averaged over the levels of: soil_root, precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.
emmeans(SG_combin_root_shoot_model, pairwise~life_stage|soil_root|precip)
## $emmeans
## soil_root = L.B, precip = A:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant   2.058 0.374  92.1    1.316     2.80
##  seedling    0.815 0.300  78.7    0.218     1.41
## 
## soil_root = S.B, precip = A:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant   1.523 0.528 109.8    0.477     2.57
##  seedling    1.055 0.300  78.7    0.459     1.65
## 
## soil_root = L.R, precip = A:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant   2.099 0.339  86.2    1.426     2.77
##  seedling    1.078 0.300  78.7    0.481     1.67
## 
## soil_root = L.B, precip = D:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant   2.106 0.392  98.2    1.327     2.88
##  seedling    1.439 0.323  85.6    0.796     2.08
## 
## soil_root = S.B, precip = D:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant   3.667 0.881 100.4    1.918     5.42
##  seedling    1.010 0.353  90.9    0.309     1.71
## 
## soil_root = L.R, precip = D:
##  life_stage emmean    SE    df lower.CL upper.CL
##  germinant   2.588 0.454  97.6    1.687     3.49
##  seedling    1.539 0.359  82.8    0.824     2.25
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## soil_root = L.B, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    1.243 0.479 58.3 2.595   0.0120 
## 
## soil_root = S.B, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.468 0.607 84.7 0.770   0.4432 
## 
## soil_root = L.R, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    1.021 0.452 52.6 2.259   0.0280 
## 
## soil_root = L.B, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.667 0.508 63.4 1.312   0.1942 
## 
## soil_root = S.B, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    2.657 0.950 91.5 2.798   0.0063 
## 
## soil_root = L.R, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    1.049 0.579 75.6 1.811   0.0741 
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Root:Shoot
Df Resid.Df F.value P.value
soil_root 2 113 0.46 0.635
precip 1 113 6.44 0.013
life_stage 1 113 23.46 0.000
soil_root:precip 2 113 0.63 0.532
soil_root:life_stage 2 113 0.47 0.624
precip:life_stage 1 113 1.25 0.266
soil_root:precip:life_stage 2 113 2.36 0.099
### Presence Combined Root:Sho ot

Diagnostic Graphs

data_SG_root_shoot_trt_comb_pres=subset(data_SG_root_shoot_trt_comb, root_association =="B")
SG_combin_root_shoot_pres_model= lmer(sqrt(root_shoot)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_root_shoot_trt_comb_pres)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_combin_root_shoot_pres_model))

## 251  20 
##  35   2
hist(resid(SG_combin_root_shoot_pres_model))

shapiro.test(resid(SG_combin_root_shoot_pres_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_root_shoot_pres_model)
## W = 0.96226, p-value = 0.01852

Raw Statistical output

anova(SG_combin_root_shoot_pres_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                             Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## soil_root                    3.820   3.820     1    72  3.0989 0.0825929 .  
## precip                      19.930  19.930     1    72 16.1697 0.0001411 ***
## life_stage                  33.827  33.827     1    72 27.4445 1.542e-06 ***
## soil_root:precip             7.796   7.796     1    72  6.3251 0.0141377 *  
## soil_root:life_stage         5.247   5.247     1    72  4.2571 0.0426929 *  
## precip:life_stage            9.546   9.546     1    72  7.7446 0.0068752 ** 
## soil_root:precip:life_stage 20.164  20.164     1    72 16.3594 0.0001300 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_root_shoot_pres_model, pairwise~precip*life_stage)
## NOTE: Results may be misleading due to involvement in interactions
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
##  precip life_stage emmean    SE   df lower.CL upper.CL
##  A      germinant    2.59 0.311 42.1     1.97     3.22
##  D      germinant    4.67 0.481 47.4     3.71     5.64
##  A      seedling     1.84 0.203 14.9     1.41     2.27
##  D      seedling     2.22 0.230 20.8     1.74     2.70
## 
## Results are averaged over the levels of: soil_root 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                  estimate    SE   df t.ratio p.value
##  A,germinant - D,germinant   -2.079 0.563 72.0 -3.696  0.0023 
##  A,germinant - A,seedling     0.750 0.371 14.8  2.022  0.2242 
##  A,germinant - D,seedling     0.372 0.387 15.4  0.962  0.7723 
##  D,germinant - A,seedling     2.830 0.522 26.4  5.425  0.0001 
##  D,germinant - D,seedling     2.451 0.533 25.5  4.601  0.0005 
##  A,seedling - D,seedling     -0.378 0.307 66.9 -1.234  0.6075 
## 
## Results are averaged over the levels of: soil_root 
## Note: contrasts are still on the sqrt scale 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 4 estimates
emmeans(SG_combin_root_shoot_pres_model, pairwise~life_stage|soil_root|precip)
## Note: Use 'contrast(regrid(object), ...)' to obtain contrasts of back-transformed estimates
## $emmeans
## soil_root = L.B, precip = A:
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant    3.01 0.361 53.5     2.29     3.74
##  seedling     1.66 0.287 43.2     1.08     2.23
## 
## soil_root = S.B, precip = A:
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant    2.18 0.509 69.0     1.16     3.19
##  seedling     2.03 0.287 43.2     1.45     2.61
## 
## soil_root = L.B, precip = D:
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant    3.09 0.378 58.5     2.33     3.84
##  seedling     2.50 0.310 48.3     1.88     3.12
## 
## soil_root = S.B, precip = D:
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant    6.26 0.881 60.4     4.50     8.02
##  seedling     1.94 0.339 52.5     1.26     2.62
## 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the sqrt (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
## soil_root = L.B, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    1.356 0.461 28.7 2.941   0.0064 
## 
## soil_root = S.B, precip = A:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.145 0.584 46.8 0.248   0.8051 
## 
## soil_root = L.B, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.585 0.489 31.9 1.197   0.2401 
## 
## soil_root = S.B, precip = D:
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    4.318 0.944 52.6 4.575   <.0001 
## 
## Note: contrasts are still on the sqrt scale 
## Degrees-of-freedom method: kenward-roger

Formatted Anova table

Total biomass
Df Resid.Df F.value P.value
soil_root 1 72 3.10 0.083
precip 1 72 16.17 0.000
life_stage 1 72 27.44 0.000
soil_root:precip 1 72 6.33 0.014
soil_root:life_stage 1 72 4.26 0.043
precip:life_stage 1 72 7.74 0.007
soil_root:precip:life_stage 1 72 16.36 0.000

Origin Combined Root:Shoot

Diagnostic Graphs

data_SG_root_shoot_trt_comb_orig=subset(data_SG_root_shoot_trt_comb, soil_status =="L")
SG_combin_root_shoot_orig_model= lmer(log(root_shoot+0.001)~soil_root*precip*life_stage+(1|block/life_stage), data= data_SG_root_shoot_trt_comb_orig)
## boundary (singular) fit: see ?isSingular
qqPlot(resid(SG_combin_root_shoot_orig_model))

## 531  51 
##  70  20
hist(resid(SG_combin_root_shoot_orig_model))

shapiro.test(resid(SG_combin_root_shoot_orig_model))
## 
##  Shapiro-Wilk normality test
## 
## data:  resid(SG_combin_root_shoot_orig_model)
## W = 0.97332, p-value = 0.0558

Raw Statistical output

anova(SG_combin_root_shoot_orig_model)
## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## soil_root                    1.0674  1.0674     1    84  0.8207 0.3675748    
## precip                       3.5647  3.5647     1    84  2.7406 0.1015573    
## life_stage                  21.4534 21.4534     1    84 16.4939 0.0001091 ***
## soil_root:precip             0.1050  0.1050     1    84  0.0807 0.7770479    
## soil_root:life_stage         0.0350  0.0350     1    84  0.0269 0.8700175    
## precip:life_stage            0.4067  0.4067     1    84  0.3127 0.5775090    
## soil_root:precip:life_stage  0.4929  0.4929     1    84  0.3790 0.5398262    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
emmeans(SG_combin_root_shoot_orig_model, pairwise~life_stage)
## NOTE: Results may be misleading due to involvement in interactions
## $emmeans
##  life_stage emmean    SE   df lower.CL upper.CL
##  germinant    2.21 0.196 10.4    1.778     2.65
##  seedling     1.22 0.158  5.8    0.827     1.61
## 
## Results are averaged over the levels of: soil_root, precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log(mu + 0.001) (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast             estimate    SE   df t.ratio p.value
##  germinant - seedling    0.995 0.252 4.03 3.949   0.0166 
## 
## Results are averaged over the levels of: soil_root, precip 
## Degrees-of-freedom method: kenward-roger 
## Results are given on the log (not the response) scale.

Formatted Anova table

Total biomass
Df Resid.Df F.value P.value
soil_root 1 84 0.82 0.368
precip 1 84 2.74 0.102
life_stage 1 84 16.49 0.000
soil_root:precip 1 84 0.08 0.777
soil_root:life_stage 1 84 0.03 0.870
precip:life_stage 1 84 0.31 0.578
soil_root:precip:life_stage 1 84 0.38 0.540